Discovery of generalized association rules with multiple minimum supports

Chung Leung Lui, Fu Lai Korris Chung

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

25 Citations (Scopus)

Abstract

Mining association rules at multiple concept levels leads to the discovery of more concrete knowledge. Nevertheless, setting an appropriate minsup for cross-level itemsets is still a non-trivial task. Besides, the mining process is computationally expensive and produces many redundant rules. In this work, we propose an efficient algorithm for mining generalized association rules with multiple minsup. The method scans appropriately k+1 times of the number of original transactions and once of the extended transactions where k is the largest itemset size. Encouraging simulation results were reported.
Original languageEnglish
Title of host publicationPrinciples of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings
PublisherSpringer Verlag
Pages510-515
Number of pages6
ISBN (Print)9783540410669
Publication statusPublished - 1 Jan 2000
Event4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000 - Lyon, France
Duration: 13 Sep 200016 Sep 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1910
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000
Country/TerritoryFrance
CityLyon
Period13/09/0016/09/00

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this