A summary of genetic-fuzzy data mining techniques

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this article, we have introduced some geneticfuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the types of fuzzy mining problems and the ways of processing items. Then, each of the four kinds of problems has been described with some approaches given.

Original languageEnglish
Title of host publicationProceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010
Pages28-29
Number of pages2
DOIs
StatePublished - 1 Nov 2010
Event2010 IEEE International Conference on Granular Computing, GrC 2010 - San Jose, CA, United States
Duration: 14 Aug 201016 Aug 2010

Publication series

NameProceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010

Conference

Conference2010 IEEE International Conference on Granular Computing, GrC 2010
CountryUnited States
CitySan Jose, CA
Period14/08/1016/08/10

Keywords

  • Data miining
  • Fuzzy data mining
  • Fuzzy set theory
  • Genetic algorithms
  • Genetic-fuzzy data mining

Fingerprint Dive into the research topics of 'A summary of genetic-fuzzy data mining techniques'. Together they form a unique fingerprint.

Cite this