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An Effective Boolean Algorithm for Mining Association Rules in Large Databases.

Suh-Ying Wur, Yungho Leu: An Effective Boolean Algorithm for Mining Association Rules in Large Databases. DASFAA 1999: 179-186
@inproceedings{DBLP:conf/dasfaa/WurL99,
  author    = {Suh-Ying Wur and
               Yungho Leu},
  editor    = {Arbee L. P. Chen and
               Frederick H. Lochovsky},
  title     = {An Effective Boolean Algorithm for Mining Association Rules in
               Large Databases},
  booktitle = {Database Systems for Advanced Applications, Proceedings of the
               Sixth International Conference on Database Systems for Advanced
               Applications (DASFAA), April 19-21, Hsinchu, Taiwan},
  publisher = {IEEE Computer Society},
  year      = {1999},
  isbn      = {0-7695-0084-6},
  pages     = {179-186},
  ee        = {db/conf/dasfaa/WurL99.html},
  crossref  = {DBLP:conf/dasfaa/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

In this paper, we present an effective Boolean algorithm for mining association rules in large databases of sales transactions. Like the Apriori algorithm, the proposed Boolean algorithm mines association rules in two steps. In the first step, logic OR and AND operations are used to compute frequent itemsets. In the second step, logic AND and XOR operations are applied to derive all interesting association rules based on the computed frequent itemsets. By only scanning the database once and avoiding generating candidate itemsets in computing frequent itemsets, the Boolean algorithm gains a significant performance improvement over the Apriori algorithm. We propose two efficient implementations of the Boolean algorithm, the BitStream approach and the Sparse-Matrix approach. Through comprehensive experiments, we show that both the BitStream approach and the Sparse-Martrix approach outperform the Apriori algorithm in all database settings. Especially, the Sparse-Matrix approach shows a very significant performance improvement over the Apriori algorithm.

Copyright © 1999 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

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