=== Run information ===

Scheme:       weka.associations.AprioriSets -N 10 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -B 0 -Z 0
Relation:     weka-peroformance-EventFilter-A-T0.05-N2-I-D-S-weka.filters.DiscretizeFilter-O-B10-Rfirst-last
Instances:    66
Attributes:   354
              [list of attributes omitted]
=== Associator model (full training set) ===

Required Attributes in Antecedents:
none

Required Attributes in Consequents:
none

option-U=-U ==> option-S=-S  [Conf: 1.0, Sup: 0.969697]
option-S=-S ==> option-U=-U  [Conf: 0.969697, Sup: 0.969697]
option-R=-R ==> option-S=-S  [Conf: 1.0, Sup: 1.0]
option-S=-S ==> option-R=-R  [Conf: 1.0, Sup: 1.0]
option-R=-R && option-U=-U ==> option-S=-S  [Conf: 1.0, Sup: 0.969697]
option-S=-S && option-U=-U ==> option-R=-R  [Conf: 1.0, Sup: 0.969697]
option-S=-S && option-R=-R ==> option-U=-U  [Conf: 0.969697, Sup: 0.969697]
option-U=-U ==> option-S=-S && option-R=-R  [Conf: 1.0, Sup: 0.969697]
option-R=-R ==> option-S=-S && option-U=-U  [Conf: 0.969697, Sup: 0.969697]
option-S=-S ==> option-R=-R && option-U=-U  [Conf: 0.969697, Sup: 0.969697]
option-U=-U ==> option-R=-R  [Conf: 1.0, Sup: 0.969697]
option-R=-R ==> option-U=-U  [Conf: 0.969697, Sup: 0.969697]

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For comparison, the pre time sequence version generated 51732
candidate itemsets for k=1 with the same data set.
-----
Debug info generated:
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Beginning to mine...
Level 1 candidates: 3726
ARMinerApriori.evaluateCandidates: writing itemset = {10 }/[1.0/66] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {10 }/[1.0/66] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 }/[1.0/66] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {11 }/[1.0/66] (1) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {29 }/[0.969697/64] (1)
ARMinerApriori.evaluateCandidates: adding itemset = {29 }/[0.969697/64] (1) to large and frequent collections.
 # of frequent itemsets for level: 1 = 3
` # of Generated candidates for level: 2 = 3
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 }/[1.0/66] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 }/[1.0/66] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {10 29 }/[0.969697/64] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {10 29 }/[0.969697/64] (2) to large and frequent collections.
ARMinerApriori.evaluateCandidates: writing itemset = {11 29 }/[0.969697/64] (2)
ARMinerApriori.evaluateCandidates: adding itemset = {11 29 }/[0.969697/64] (2) to large and frequent collections.
 # of frequent itemsets for level: 2 = 3
` # of Generated candidates for level: 3 = 1
ARMinerApriori.evaluateCandidates: writing itemset = {10 11 29 }/[0.969697/64] (3)
ARMinerApriori.evaluateCandidates: adding itemset = {10 11 29 }/[0.969697/64] (3) to large and frequent collections.
 # of frequent itemsets for level: 3 = 1
 # of Generated candidates for level: 4 = 0
SET.insert: itemset = {10 }/[1.0/66] (1)
SET.insert: itemset = {11 }/[1.0/66] (1)
SET.insert: itemset = {29 }/[0.969697/64] (1)
SET.insert: itemset = {10 11 }/[1.0/66] (2)
SET.insert: itemset = {10 29 }/[0.969697/64] (2)
SET.insert: itemset = {11 29 }/[0.969697/64] (2)
SET.insert: itemset = {10 11 29 }/[0.969697/64] (3)
SET.getSupport: of itemset: {29 }/[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 }/[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: {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 = 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 29 }/[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: {10 29 }/[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: {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: {29 }/[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 = 1.0
SET.getSupport: of itemset: {29 }/[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 }/[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 ] )