The Improvement and Application of the Fuzzy K-Prototypes Algorithm

Author

Fan, Lilin

Author_Institution

Coll. of Comput. & Inf. Technol., Henan Normal Univ., Xinxiang, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Fuzzy K-prototypes is a very efficient algorithm for processing large scale mixed data set, but the selection of initial clustering center has an important impact on the clustering effect of algorithm. FKP algorithm is improved by using genetic algorithm in this paper. Seeking the initial clustering center for fuzzy K-prototypes algorithm by using genetic algorithm overcomes the shortcoming effectively, which has been applied to customer data segmentation of automobile industry collaborative platform. It is proved that the improved effect is obviously.

Keywords

automobile industry; customer services; data handling; fuzzy set theory; genetic algorithms; groupware; pattern clustering; automobile industry collaborative platform; customer data segmentation; fuzzy K-prototypes algorithm; genetic algorithm; initial clustering center selection; mixed data set processing; Application software; Biological cells; Clustering algorithms; Convergence; Cost function; Equations; Fuzzy sets; Genetic algorithms; Iterative algorithms; Large-scale systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5364736

Filename

5364736