Possibilistic approach to clustering of interval data

Author

Pimentel, Bruno Almeida ; de Souza, Renata M. C. R.

Author_Institution

Centro de Inf., UFPE, Recife, Brazil

fYear

2012

fDate

14-17 Oct. 2012

Firstpage

190

Lastpage

195

Abstract

Clustering analysis is an important tool used in several application domains like pattern recognition, computer vision and computational biology to summarize data. The fuzzy c-means method (FCM) is the most popular fuzzy clustering algorithm, however this method is sensitive to noisy data. The possibilistic c-means (PCM) was created as an alternative to solve this problem. The propose in this work is extend the classical PCM to symbolic interval-valued data. Experiments with artificial and real symbolic interval-type data sets are performed and the results show the superiority of PCM in relation to FCM methods to interval-valued data.

Keywords

fuzzy set theory; pattern clustering; FCM methods; artificial symbolic interval-type data sets; clustering analysis; computational biology; computer vision; fuzzy c-mean method; fuzzy clustering algorithm; interval data clustering; noisy data; pattern recognition; possibilistic approach; possibilistic c-mean method; real symbolic interval-type data sets; Clustering algorithms; Clustering methods; Equations; Muscles; Phase change materials; Prototypes; Vectors; Fuzzy C-Means; Interval data; Noisy data; Pattern recognition; Possibilistic C-Means;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4673-1713-9

Electronic_ISBN

978-1-4673-1712-2

Type

conf

DOI

10.1109/ICSMC.2012.6377698

Filename

6377698