A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports

Chun Hao Chen*, Tzung Pei Hong, S. Tseng

*Corresponding author for this work

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

4 Scopus citations

Abstract

In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and fuzzy association rules form quantitative transactions. The evaluation process might take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the Cluster-based Genetic-Fuzzy mining approach for items with Multiple Minimum Supports (CGFMMS), is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. Experimental results also show the effectiveness and the efficiency of the proposed approach.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Pages864-869
Number of pages6
DOIs
StatePublished - 9 Jun 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
CountryJapan
CityOsaka
Period20/05/0823/05/08

Keywords

  • Data mining
  • Fuzzy set
  • Genetic algorithm
  • Genetic-fuzzy mining
  • K-means clustering
  • Multiple minimum supports
  • Requirement satisfaction

Fingerprint Dive into the research topics of 'A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports'. Together they form a unique fingerprint.

  • Cite this

    Chen, C. H., Hong, T. P., & Tseng, S. (2008). A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports. In Advances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings (pp. 864-869). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5012 LNAI). https://doi.org/10.1007/978-3-540-68125-0_85