An effective algorithm for mining interesting quantitative association rules

Chun Chung Chan, Wai Ho Au

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

36 Citations (Scopus)

Abstract

In this paper, we describe a novel technique, called APACS2, for mining interesting quantitative association rules from very large databases. To effectively mine such rules. APACS2 employs adjusted difference analysis. The use of this technique has the advantage that it does not require any user- supplied thresholds which are often hard to determine. Furthermore, APACS2 also has the advantage that it allows users to discover both positive and negative association rules. A positive association rule tells us that a record having certain attribute value will also have another attribute value whereas a negative association rule tells us that a record having certain attribute value will not have another attribute value. The fact that APACS2 is able to mine both positive and negative association rules and that it uses an objective yet meaningful measure to determine the interestingness of a rule makes it very effective at different data mining tasks.
Original languageEnglish
Title of host publicationProceedings of the 1997 ACM Symposium on Applied Computing, SAC 1997
PublisherAssociation for Computing Machinery
Pages88-90
Number of pages3
ISBN (Print)0897918509, 9780897918503
DOIs
Publication statusPublished - 1 Jan 1997
Event1997 ACM Symposium on Applied Computing, SAC 1997 - San Jose, CA, United States
Duration: 28 Feb 19971 Mar 1997

Conference

Conference1997 ACM Symposium on Applied Computing, SAC 1997
Country/TerritoryUnited States
CitySan Jose, CA
Period28/02/971/03/97

Keywords

  • Data mining
  • Interestingness measure
  • Negative association rules
  • Positive association rules
  • Quantitative association rules

ASJC Scopus subject areas

  • Software

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