Effective algorithm for discovering fuzzy rules in relational databases

Wai Ho Au, Chun Chung Chan

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

74 Citations (Scopus)

Abstract

In this paper, we present a novel technique, called F-APACS, for discovering fuzzy association rules in relational databases. Instead of dividing up quantitative attributes into fixed intervals and searching for rules expressed in terms of them, F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The definitions of these linguistic terms are based on fuzzy set theory and the association rules expressed in them are, therefore, called fuzzy association rules here. To discover these rules, F-APACS utilizes an objective interestingness measure when determining if two attribute values are related. This measure is defined in terms of an `adjusted difference' between observed and expected frequency counts. The use of such a measure has the advantage that no user-supplied thresholds are required. In addition to this interestingness measure, F-APACS has another unique feature that it provides a mechanism to allow quantitative values be inferred from the rules. Such feature, as shown here, make F-APACS very effective at various mining tasks.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE
Pages1314-1319
Number of pages6
Publication statusPublished - 1 Jan 1998
EventProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2) - Anchorage, AK, United States
Duration: 4 May 19989 May 1998

Conference

ConferenceProceedings of the 1998 IEEE International Conference on Fuzzy Systems,. Part 2 (of 2)
Country/TerritoryUnited States
CityAnchorage, AK
Period4/05/989/05/98

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

Cite this