Genetic-fuzzy data mining techniques

Tzung Pei Hong; Chun Hao Chen; S. Tseng

doi:10.1109/GrC.2010.157

Genetic-fuzzy data mining techniques

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

^*Corresponding author for this work

Institute of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 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 language	English
Title of host publication	Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010
Pages	26-27
Number of pages	2
DOIs	https://doi.org/10.1109/GrC.2010.157
State	Published - 1 Nov 2010
Event	2010 IEEE International Conference on Granular Computing, GrC 2010 - San Jose, CA, United States Duration: 14 Aug 2010 → 16 Aug 2010

Publication series

Name	Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010

Conference

Conference	2010 IEEE International Conference on Granular Computing, GrC 2010
Country	United States
City	San Jose, CA
Period	14/08/10 → 16/08/10

Keywords

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

Access to Document

10.1109/GrC.2010.157

Link to publication in Scopus

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Cite this

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title = "Genetic-fuzzy data mining techniques",

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.",

keywords = "Data miining, Fuzzy data mining, Fuzzy set theory, Genetic algorithms, Genetic-fuzzy data mining",

author = "Hong, {Tzung Pei} and Chen, {Chun Hao} and S. Tseng",

year = "2010",

month = nov,

day = "1",

doi = "10.1109/GrC.2010.157",

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isbn = "9780769541617",

series = "Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010",

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booktitle = "Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010",

note = "null ; Conference date: 14-08-2010 Through 16-08-2010",

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Hong, TP, Chen, CH & Tseng, S 2010, Genetic-fuzzy data mining techniques. in Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010., 5576249, Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010, pp. 26-27, 2010 IEEE International Conference on Granular Computing, GrC 2010, San Jose, CA, United States, 14/08/10. https://doi.org/10.1109/GrC.2010.157

Genetic-fuzzy data mining techniques. / Hong, Tzung Pei; Chen, Chun Hao; Tseng, S.

Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010. 2010. p. 26-27 5576249 (Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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