Clustering of fuzzy data using credibilistic expected and critical values

Author

Sampath, S. ; Kumar, R. Senthil

Author_Institution

Dept. of Stat., Univ. of Madras, Chennai, India

fYear

2014

Firstpage

176

Lastpage

181

Abstract

This paper introduces a new approach for handling fuzzy data sets in producing crisp clusters. The proposed method uses the notion of expectation of discrete fuzzy variables. The proposed method has been compared with another approach through fuzzy critical values pursued by Sampath and Kalaivani (2010). Comparative experimental study has been carried out with the help of data sets simulated from multivariate normal populations where fuzziness has been induced using a well defined procedure. In the process of comparison two partitioning clustering methods, namely, k-means and k-medoids algorithm have been considered.

Keywords

fuzzy set theory; learning (artificial intelligence); pattern clustering; credibilistic expected value; critical value; discrete fuzzy variables; fuzzy data clustering; fuzzy data set handling; k-means algorithm; k-medoids algorithm; multivariate normal population; partitioning clustering method; Classification algorithms; Clustering algorithms; Computers; Conferences; Fuzzy logic; Partitioning algorithms; Prototypes; credibility expectation; credibility measure; critical values; k-means; k-medoids; silhouette value;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication and Systems, 2014 International Conference on

Print_ISBN

978-1-4799-3671-7

Type

conf

DOI

10.1109/ICCCS.2014.7068189

Filename

7068189