Apriori Sets And Sequences - Keith's MS Thesis
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Temporal

Intro ] [ Efficient Mining of Intertransaction Association Rules-Notes ] [ Mining Inter Transaction Associations with Templates-Notes ] [ Mining Inter Transaction Associations with Templates.pdf ]

Up: References ]

Thoughts on:

      "Mining Inter-Transaction Associations with Templates",
      Ling Feng, Hongjun Lu, Jeffery Xu Yu, Jiawei Han
      Hong Kong Polytechnic University, 
      Hong Kong University of Science and Technology,
      Australian National University, Simon Fraser University,
      CIKM 1999

      
The template here is a template for a rule.

If each price/day/stock included more than the closing price, like the 
price every 30 seconds, it would look more like our data. The interesting
thing in this paper is the idea of mining for rules across instances.
      
Their example: 

      If the prices of IBM and SUN go up, Microsoft's will most likely go up
      the next day.
      
Here, each day is an instance and the movement of a stock's price is an 
attribute. If one of their instances corresponds loosely to taking a 
single time slice from our time series data, it might be possible
to adapt their method to our data. This used in conjunction with
similarity methods for finding support for rules found among all our
instances might be an interesting approach.
Intro
A Framwork for Tempora Data Mining-Notes
A Survey of Temporal Knowledge Discovery Paradigms and Methods-Notes
A Survey of Temporal Knowledge Discovery Paradigms and Methods.pdf
Adding Temporal Semantics to Association Rules-Notes
An Approach To Discovering Temporal Association Rules
An Approach to Discovering Temporal Association Rules-Notes
An Approach to Discovering Temporal Association Rules.PDF
Beyond Intratransaction Association Analysis-Mining Multidimensional Intertransaction Association Rules.pdf
Data Mining Introduction and Advanced Topics-Notes
Detecting Complex Dependencies in Categorical Data-Notes
Detecting Complex Dependencies in Categorical Data.pdf
Discovering Calendar-based Temporal Association Rules.pdf
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences-Notes
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences.pdf
Discovering Temporal Association Rules-Algorithms Language and System.pdf
Discovering Temporal Association Rules in Temporal Databases-Notes
Discovering Temporal Patterns in Multiple Granularities-Notes
Discovering frequent episodes in sequences (Extended Abstract).ps
Discovery of Association Rules in Temporal Databases.ps
Discovery of Frequent Episodes in Event Sequences.pdf
Discovery of frequent episodes in event sequences-Notes
Discovery of frequent episodes in event sequences.ps
Efficient Mining of Intertransaction Association Rules-Notes
Mining Inter Transaction Associations with Templates-Notes
Mining Inter Transaction Associations with Templates.pdf
Mining Temporal Features in Association Rules-Notes
On Mining General Temoral Association Rules in a Publicaton Database.pdf
On the Discovery of Interesting Patterns in Association Rules.pdf
Representing Temporal Relationships Between Events and Their Effects.doc
Rule Discovery From Time Series-Notes
Testing Complex Temporal Relationships Involving Multiple Granularities and Its Application to Data Mining (Extended Abstract).pdf
roddick.pdf
roddick.ps

by: Keith A. Pray
Last Modified: July 4, 2004 7:21 AM
© 2004 - 1975 Keith A. Pray.
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