Mining quantitative association rules under inequality constraints

Charles Lo, Vincent To Yee Ng

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

Abstract

In the past several years, there has been much active work in developing algorithms for mining association rules. However, in many real-life situations, not all association rules are of interest to the user. A user may want to find association rules which satisfy a given inequality constraint for a set of quantitative items. In other words, users are more interested in the subsets of those associations. We present how to integrate the inequality constraints into the mining process and reduce the number of database scannings. The algorithm we present generates the large itemsets by building the expression tree and prunes away the undesired one by checking the acceptance range. In our work, we consider constraints of arithmetic inequalities which are composed of common operators such as and /. Preliminary experimental results of the algorithm in comparison with the classical a-priori algorithm are also reported.
Original languageEnglish
Title of host publicationProceedings - 1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999
PublisherIEEE
Pages53-59
Number of pages7
ISBN (Electronic)0769504531, 9780769504537
DOIs
Publication statusPublished - 1 Jan 1999
Event1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999 - Chicago, United States
Duration: 7 Nov 1999 → …

Conference

Conference1999 Workshop on Knowledge and Data Engineering Exchange, KDEX 1999
CountryUnited States
CityChicago
Period7/11/99 → …

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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