Projected Rough Fuzzy c-means clustering

Author

Puri, C. ; Kumar, Naveen

Author_Institution

Dept. of Comput. Sci., Univ. of Delhi, Delhi, India

fYear

2011

fDate

22-24 Nov. 2011

Firstpage

530

Lastpage

536

Abstract

The conventional rough set based feature selection techniques find the relevant features for the entire data set. However different sets of dimensions may be relevant for different clusters. This paper introduces a novel Projected Rough Fuzzy c-means clustering algorithm (PRFCM) which employs rough sets to model uncertainty in data, and fuzzy set theory to compute the weights of dimensions applicable to individual clusters. We discuss the convergence of the proposed algorithm and present the results of applying the proposed approach to several UCI data sets to demonstrate that it scores over its competitors in terms of several quality and validity measures.

Keywords

data analysis; feature extraction; fuzzy set theory; pattern clustering; rough set theory; data uncertainty; fuzzy set theory; projected rough fuzzy c-means clustering; rough set based feature selection; rough sets; Conferences; Decision support systems; Intelligent systems; Mercury (metals); Radio frequency; Convergence; fuzzy clustering; projected clustering; rough sets; validity measures;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on

Conference_Location

Cordoba

ISSN

2164-7143

Print_ISBN

978-1-4577-1676-8

Type

conf

DOI

10.1109/ISDA.2011.6121710

Filename

6121710