ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY

Authors:
Xiaowei Yan;Shichao Zhang;Chengqi Zhang
Affiliations:
Computer Department, Guangxi Normal University, China;Computer Department, Guangxi Normal University, China;Faculty of Information Technology, University of Technology, Sydney, Australia
Venue:
Applied Artificial Intelligence
Year:
2007

Citing 19
Cited 0

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Abstract

Systematically we study data structures used to implement the algorithms of association rule mining, including hash tree, itemset tree, and FP-tree (frequent pattern tree). Further, we present a generalized FP-tree in an applied context. This assists in better understanding existing association-rule-mining strategies. In addition, we discuss and analyze experimentally the generalized k-FP-tree, and demonstrate that the generalized FP-tree reduces the computation costs significantly. This study will be useful to many association analysis tasks where one must provide really interesting rules and develop efficient algorithms for identifying association rules.