An effective hash-based algorithm for mining association rules

• Jong Soo Park

  IBM Thomas J. Watson Research Center, Yorktown Heights, New York

• Ming-Syan Chen

  IBM Thomas J. Watson Research Center, Yorktown Heights, New York

• Philip S. Yu

  IBM Thomas J. Watson Research Center, Yorktown Heights, New York

抄録

In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k -itemset in increasing order of k where a large k -itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.

収録刊行物

• ACM SIGMOD Record

  ACM SIGMOD Record 24 (2), 175-186, 1995-05-22

  Association for Computing Machinery (ACM)

キーワード

• Information Systems
• Software

詳細情報

• CRID: 1360292620034296704
• NII論文ID: 80008376173
• DOI: 10.1145/568271.223813
• ISSN: 01635808
• Web Site: https://dl.acm.org/doi/pdf/10.1145/568271.223813
• データソース種別:
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  • CiNii Articles