Generating non-redundant association rules

Mohammed J. Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

310 Citations (Scopus)

Abstract

The traditional association rule mining framework produces many redundant rules. The extent of redundancy is a lot larger than previously suspected. We present a new framework for associations based on the concept of closed frequent itemsets. The number of non-redundant rules produced by the new approach is exponentially (in the length of the longest frequent itemset) smaller than the rule set from the traditional approach. Experiments using several "hard" as well as "easy" real and synthetic databases confirm the utility of our framework in terms of reduction in the number of rules presented to the user, and in terms of time.

Original languageEnglish
Title of host publicationProceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsR. Ramakrishnan, S. Stolfo, R. Bayardo, I. Parsa, R. Ramakrishnan, S. Stolfo, R. Bayardo, I. Parsa
Pages34-43
Number of pages10
Publication statusPublished - 1 Dec 2000
Externally publishedYes
EventProceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - Boston, MA, United States
Duration: 20 Aug 200023 Aug 2000

Other

OtherProceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)
CountryUnited States
CityBoston, MA
Period20/8/0023/8/00

Fingerprint

Association rules
Redundancy
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Zaki, M. J. (2000). Generating non-redundant association rules. In R. Ramakrishnan, S. Stolfo, R. Bayardo, I. Parsa, R. Ramakrishnan, S. Stolfo, R. Bayardo, ... I. Parsa (Eds.), Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 34-43)

Generating non-redundant association rules. / Zaki, Mohammed J.

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / R. Ramakrishnan; S. Stolfo; R. Bayardo; I. Parsa; R. Ramakrishnan; S. Stolfo; R. Bayardo; I. Parsa. 2000. p. 34-43.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zaki, MJ 2000, Generating non-redundant association rules. in R Ramakrishnan, S Stolfo, R Bayardo, I Parsa, R Ramakrishnan, S Stolfo, R Bayardo & I Parsa (eds), Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 34-43, Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), Boston, MA, United States, 20/8/00.
Zaki MJ. Generating non-redundant association rules. In Ramakrishnan R, Stolfo S, Bayardo R, Parsa I, Ramakrishnan R, Stolfo S, Bayardo R, Parsa I, editors, Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000. p. 34-43
Zaki, Mohammed J. / Generating non-redundant association rules. Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / R. Ramakrishnan ; S. Stolfo ; R. Bayardo ; I. Parsa ; R. Ramakrishnan ; S. Stolfo ; R. Bayardo ; I. Parsa. 2000. pp. 34-43
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