SARM - Succinct association rule mining: An approach to enhance association mining

Jitender Deogun, Liying Jiang

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

3 Scopus citations

Abstract

The performance of association rule mining in terms of computation time and number of redundant rules generated deteriorates as the size of database increases and/or support threshold used is smaller. In this paper, we present a new approach called SARM - succinct association rule mining, to enhance the association mining. Our approach is based on our understanding of the mining process that items become less useful as mining proceeds, and that such items can be eliminated to accelerate the mining and to reduce the number of redundant rules generated. We propose a new paradigm that an item becomes less useful when the most interesting rules involving the item have been discovered and deleting it from the mining process will not result in any significant loss of information. SARM generates a compact set of rules called succinct association rule (SAR) set that is largely free of redundant rules. SARM is efficient in association mining, especially when support threshold used is small. Experiments are conducted on both synthetic and real-life databases. SARM approach is especially suitable for applications where rules with small support may be of significant interest. We show that for such applications SAR set can be mined efficiently.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 15th International Symposium, ISMIS 2005, Proceedings
PublisherSpringer Verlag
Pages121-130
Number of pages10
ISBN (Print)3540258787, 9783540258780
DOIs
StatePublished - 2005
Event15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 - Saratoga Springs, NY, United States
Duration: May 25 2005May 28 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3488 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005
CountryUnited States
CitySaratoga Springs, NY
Period5/25/055/28/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Deogun, J., & Jiang, L. (2005). SARM - Succinct association rule mining: An approach to enhance association mining. In Foundations of Intelligent Systems - 15th International Symposium, ISMIS 2005, Proceedings (pp. 121-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3488 LNAI). Springer Verlag. https://doi.org/10.1007/11425274_13