A comparative study between fuzzy c-means and ckMeans algorithms

Author

De Vargas, Rogério R. ; Bedregal, Benjamín R C

Author_Institution

Dept. of Inf. & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil

fYear

2010

fDate

12-14 July 2010

Firstpage

1

Lastpage

6

Abstract

Clustering is a useful approach in data mining, image segmentation, and other problems of pattern recognition. Fuzzy clustering process can be quite slow when there are many objects or pattern to be clustered. This article discusses about an algorithm, ckMeans, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting partition quality. The reduction is done by calculating a new mathematical equation to obtaining center cluster. To validate the proposed methodology we compared the original fuzzy c-means algorithm with that proposed in this paper.

Keywords

fuzzy systems; pattern clustering; ckMeans algorithms; comparative study; data mining; fuzzy c-means; fuzzy clustering process; image segmentation; mathematical equation; pattern recognition; Clustering algorithms; Data engineering; Data mining; Equations; Fuzzy set theory; Image segmentation; Informatics; Mathematics; Partitioning algorithms; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American

Conference_Location

Toronto, ON

Print_ISBN

978-1-4244-7859-0

Electronic_ISBN

978-1-4244-7857-6

Type

conf

DOI

10.1109/NAFIPS.2010.5548194

Filename

5548194