Apriori Sets And Sequences : ASAS Home : Results : trace-2002-05-03-16:53

Results - Rules and Performance
[ Intro ]	[ 2002-06-26-even-better-rules ]	[ 2002-07-27-66 ]
[ 2002-07-29-99 ]	[ 2003-03-03-random-test ]	[ Test-Matrix.xls ]
[ compare-duplicate-lookup-vector-tree-1 ]	[ compare-previous-dups-no-previous-1 ]	[ purpose ]
[ reduced-k=1-2002-05-09 ]	[ trace-2002-05-03-16:53 ]	[ very first rules ever ]
[ caches ] [ old-incomplete ] [ performance-improvements ] [ promoters ] [ sleep ] [ stock-market ]
Beginning to mine...
Level 1 candidates: 13655
ARMinerApriori.evaluateCandidates: writing itemset = {7 }/[0.969697/32] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {7 }/[0.969697/32] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 }/[0.93939394/31] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {10 }/[0.93939394/31] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 }/[1.0/33] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {11 }/[1.0/33] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {12 }/[1.0/33] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {12 }/[1.0/33] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {1949 }/[0.969697/32] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {1949 }/[0.969697/32] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11423 }/[1.0/33] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {11423 }/[1.0/33] (1) to large and frequent collections.
 # of frequent itemsets for level: 1 = 6
` # of Generated candidates for level: 2 = 15
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 }/[0.969697/32] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 }/[0.969697/32] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 }/[0.969697/32] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 }/[0.969697/32] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 12 }/[0.969697/32] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {7 12 }/[0.969697/32] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 1949 }/[0.969697/32] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {7 1949 }/[0.969697/32] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11423 }/[0.969697/32] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11423 }/[0.969697/32] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 12 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 12 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 1949 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 1949 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11423 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11423 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 12 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {11 12 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 1949 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {11 1949 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 11423 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {11 11423 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {12 1949 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {12 1949 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {12 11423 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {12 11423 }/[1.0/33] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {1949 11423 }/[1.0/33] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {1949 11423 }/[1.0/33] (2) to large and frequent collections.
 # of frequent itemsets for level: 2 = 15
` # of Generated candidates for level: 3 = 20
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 12 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 12 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 1949 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 1949 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11423 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11423 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 12 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 12 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 1949 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 1949 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 11423 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 11423 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 12 1949 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 12 1949 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 12 11423 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 12 11423 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 1949 11423 }/[0.969697/32] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {7 1949 11423 }/[0.969697/32] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 12 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 12 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 1949 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 1949 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 11423 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 12 1949 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 12 1949 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 12 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 12 11423 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 1949 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 1949 11423 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 12 1949 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {11 12 1949 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 12 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {11 12 11423 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 1949 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {11 1949 11423 }/[1.0/33] (3) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {12 1949 11423 }/[1.0/33] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {12 1949 11423 }/[1.0/33] (3) to large and frequent collections.
 # of frequent itemsets for level: 3 = 20
` # of Generated candidates for level: 4 = 15
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 12 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 12 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 1949 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 1949 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 12 1949 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 12 1949 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 12 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 12 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 1949 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 1949 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 12 1949 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 12 1949 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 12 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 12 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 1949 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 1949 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 12 1949 11423 }/[0.969697/32] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {7 12 1949 11423 }/[0.969697/32] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 12 1949 }/[1.0/33] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 12 1949 }/[1.0/33] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 12 11423 }/[1.0/33] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 12 11423 }/[1.0/33] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 1949 11423 }/[1.0/33] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 1949 11423 }/[1.0/33] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 12 1949 11423 }/[1.0/33] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {10 12 1949 11423 }/[1.0/33] (4) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 12 1949 11423 }/[1.0/33] (4)
ARMinerApriori.evaluateCandidates: adding itemset = {11 12 1949 11423 }/[1.0/33] (4) to large and frequent collections.
 # of frequent itemsets for level: 4 = 15
` # of Generated candidates for level: 5 = 6
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 12 1949 }/[0.969697/32] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 12 1949 }/[0.969697/32] (5) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 12 11423 }/[0.969697/32] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 12 11423 }/[0.969697/32] (5) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 1949 11423 }/[0.969697/32] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 1949 11423 }/[0.969697/32] (5) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 12 1949 11423 }/[0.969697/32] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 12 1949 11423 }/[0.969697/32] (5) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {7 11 12 1949 11423 }/[0.969697/32] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {7 11 12 1949 11423 }/[0.969697/32] (5) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 12 1949 11423 }/[1.0/33] (5)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 12 1949 11423 }/[1.0/33] (5) to large and frequent collections.
 # of frequent itemsets for level: 5 = 6
` # of Generated candidates for level: 6 = 1
ARMinerApriori.evaluateCandidates: writing itemset = {7 10 11 12 1949 11423 }/[0.969697/32] (6)
ARMinerApriori.evaluateCandidates: adding itemset = {7 10 11 12 1949 11423 }/[0.969697/32] (6) to large and frequent collections.
 # of frequent itemsets for level: 6 = 1
 # of Generated candidates for level: 7 = 0
AprioriRules.initializeSupports: adding to supports: itemset = {7 }/[0.969697/32] (1) with support = 0.969697
SET.insert: itemset = {7 }/[0.969697/32] (1)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 }/[1.0/33] (1) with support = 1.0
SET.insert: itemset = {11 }/[1.0/33] (1)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {12 }/[1.0/33] (1) with support = 1.0
SET.insert: itemset = {12 }/[1.0/33] (1)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {1949 }/[0.969697/32] (1) with support = 0.969697
SET.insert: itemset = {1949 }/[0.969697/32] (1)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11423 }/[1.0/33] (1) with support = 1.0
SET.insert: itemset = {11423 }/[1.0/33] (1)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 }/[0.969697/32] (2) with support = 0.969697
SET.insert: itemset = {7 10 }/[0.969697/32] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 }/[0.969697/32] (2) with support = 0.969697
SET.insert: itemset = {7 11 }/[0.969697/32] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 12 }/[0.969697/32] (2) with support = 0.969697
SET.insert: itemset = {7 12 }/[0.969697/32] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 1949 }/[0.969697/32] (2) with support = 0.969697
SET.insert: itemset = {7 1949 }/[0.969697/32] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11423 }/[0.969697/32] (2) with support = 0.969697
SET.insert: itemset = {7 11423 }/[0.969697/32] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {10 11 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 12 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {10 12 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 1949 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {10 1949 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11423 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {10 11423 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 12 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {11 12 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 1949 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {11 1949 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 11423 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {11 11423 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {12 1949 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {12 1949 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {12 11423 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {12 11423 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {1949 11423 }/[1.0/33] (2) with support = 1.0
SET.insert: itemset = {1949 11423 }/[1.0/33] (2)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 10 11 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 12 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 10 12 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 1949 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 10 1949 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11423 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 10 11423 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 12 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 11 12 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 1949 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 11 1949 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 11423 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 11 11423 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 12 1949 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 12 1949 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 12 11423 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 12 11423 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 1949 11423 }/[0.969697/32] (3) with support = 0.969697
SET.insert: itemset = {7 1949 11423 }/[0.969697/32] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 12 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 11 12 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 1949 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 11 1949 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 11 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 12 1949 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 12 1949 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 12 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 12 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 1949 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {10 1949 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 12 1949 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {11 12 1949 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 12 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {11 12 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 1949 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {11 1949 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {12 1949 11423 }/[1.0/33] (3) with support = 1.0
SET.insert: itemset = {12 1949 11423 }/[1.0/33] (3)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 12 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 11 12 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 1949 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 11 1949 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 11 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 12 1949 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 12 1949 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 12 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 12 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 1949 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 10 1949 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 12 1949 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 11 12 1949 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 12 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 11 12 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 1949 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 11 1949 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 12 1949 11423 }/[0.969697/32] (4) with support = 0.969697
SET.insert: itemset = {7 12 1949 11423 }/[0.969697/32] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 12 1949 }/[1.0/33] (4) with support = 1.0
SET.insert: itemset = {10 11 12 1949 }/[1.0/33] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 12 11423 }/[1.0/33] (4) with support = 1.0
SET.insert: itemset = {10 11 12 11423 }/[1.0/33] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 1949 11423 }/[1.0/33] (4) with support = 1.0
SET.insert: itemset = {10 11 1949 11423 }/[1.0/33] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 12 1949 11423 }/[1.0/33] (4) with support = 1.0
SET.insert: itemset = {10 12 1949 11423 }/[1.0/33] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {11 12 1949 11423 }/[1.0/33] (4) with support = 1.0
SET.insert: itemset = {11 12 1949 11423 }/[1.0/33] (4)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 12 1949 }/[0.969697/32] (5) with support = 0.969697
SET.insert: itemset = {7 10 11 12 1949 }/[0.969697/32] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 12 11423 }/[0.969697/32] (5) with support = 0.969697
SET.insert: itemset = {7 10 11 12 11423 }/[0.969697/32] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 1949 11423 }/[0.969697/32] (5) with support = 0.969697
SET.insert: itemset = {7 10 11 1949 11423 }/[0.969697/32] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 12 1949 11423 }/[0.969697/32] (5) with support = 0.969697
SET.insert: itemset = {7 10 12 1949 11423 }/[0.969697/32] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 11 12 1949 11423 }/[0.969697/32] (5) with support = 0.969697
SET.insert: itemset = {7 11 12 1949 11423 }/[0.969697/32] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {10 11 12 1949 11423 }/[1.0/33] (5) with support = 1.0
SET.insert: itemset = {10 11 12 1949 11423 }/[1.0/33] (5)
SET.insert: assigned support to node = 

AprioriRules.initializeSupports: adding to supports: itemset = {7 10 11 12 1949 11423 }/[0.969697/32] (6) with support = 0.969697
SET.insert: itemset = {7 10 11 12 1949 11423 }/[0.969697/32] (6)
SET.insert: assigned support to node = 

SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.0 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.0 )
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {11 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 12 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {10 11 12 1949 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.0 )
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 12 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 12 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 12 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 12 1949 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 12 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 12 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {10 11 12 1949 11423 }/[0.0/0] (5)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11 12 1949 11423 }/[0.0/0] (5)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 12 1949 11423 }/[0.0/0] (5)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 1949 11423 }/[0.0/0] (5)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 12 11423 }/[0.0/0] (5)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 10 11 12 1949 }/[0.0/0] (5)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
SET.getSupport: of itemset: {10 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
SET.getSupport: of itemset: {10 11 1949 11423 }/[0.0/0] (4)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 11423 }/[0.0/0] (4)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 1949 }/[0.0/0] (4)Found support = 1.0
SET.getSupport: of itemset: {7 12 1949 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 11 1949 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 1949 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 1949 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 1949 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 11423 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 1949 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 12 }/[0.0/0] (4)Found support = 0.969697
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {10 11 12 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 10 11 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {10 11 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 10 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {10 }/[0.0/0] (1)Found support = 0.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 12 1949 11423 }/[0.0/0] (4)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {7 12 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 1949 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 12 11423 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {7 11 12 1949 }/[0.0/0] (4)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 0.969697 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
SET.getSupport: of itemset: {7 1949 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 12 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 11423 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 1949 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {7 11 12 }/[0.0/0] (3)Found support = 0.969697
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {7 11423 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 1949 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 12 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {7 11 }/[0.0/0] (2)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {7 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 0.969697 )
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {12 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 1949 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 12 11423 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {11 12 1949 }/[0.0/0] (3)Found support = 1.0
AprioriRules.findAssociations (weka version): is_frequent support = 1.0 is_consequent support = 0.0 is_antecedent support = 0.0 ( 1.0 )
SET.getSupport: of itemset: {1949 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {12 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 11423 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 1949 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11 12 }/[0.0/0] (2)Found support = 1.0
SET.getSupport: of itemset: {11423 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {1949 }/[0.0/0] (1)Found support = 0.969697
SET.getSupport: of itemset: {12 }/[0.0/0] (1)Found support = 1.0
SET.getSupport: of itemset: {11 }/[0.0/0] (1)Found support = 1.0
^[[B^C
~/sequence-mining-research/code/Weka-ARMiner-Time_Sequence >
by: Keith A. Pray
Last Modified: July 4, 2004 8:46 AM