Iterative mining for rules with constrained antecedents

Zheng Sun, Philip S. Yu, Xiang Yang Li

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


In this study we discuss the following association rule mining problem: for a userdefined set A of items, the objective is to compute all association rules (satisfying suitable support and confidence thresholds) induced by A, where an association rule is said to be induced by A if its antecedent (i.e., LHS) is a subset of A while the consequent (i.e., RHS) contains no items in A. In particular, we are interested in a multistep scenario where in each step A is incremented by one item and all association rules induced by the updated A are to be computed. We propose an efficient iterative algorithm that can exploit mining information gained in previous steps to efficiently answer subsequent queries.

Original languageEnglish
Title of host publicationProceedings of the 2005 SIAM International Conference on Data Mining (SDM)
EditorsHillol Kargupta, Jaideep Srivastava, Chandrika Kamath, Arnold Goodman
Place of PublicationPhiladelphia
PublisherSociety for Industrial and Applied Mathematics (SIAM)
Number of pages5
ISBN (Electronic)9781611972757
ISBN (Print)9780898715934
Publication statusPublished - 21 Apr 2005
Externally publishedYes
Event5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States
Duration: 21 Apr 200523 Apr 2005


Conference5th SIAM International Conference on Data Mining, SDM 2005
Country/TerritoryUnited States
CityNewport Beach, CA

Scopus Subject Areas

  • Software


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