An Improvement to the Possibilistic Fuzzy c-Means Clustering Algorithm

Author

Ojeda-Magaiña, B. ; Ruelas, R. ; Corona-Nakamura, M.A. ; Andina, D.

Author_Institution

DIP-CUCEI Univ. de Guadalajara, Zapopan

fYear

2006

fDate

24-26 July 2006

Firstpage

1

Lastpage

8

Abstract

In this work we propose to use the Gustafson-Kessel (GK) algorithm within the PFCM (Possibilistic Fuzzy c-Means), such that the cluster distributions have a better adaptation with the natural distribution of the data. The PFCM, proposed by Pal et al. on 2005, is founded on the fuzzy membership degrees of the FCM and the typicality values of the PCM. Nevertheless, this algorithm uses the Euclidian distance which gives circular clusters. So, incorporating the GK algorithm and the Mahalanobis measure for the calculus of the distance, we have the possibility to get ellipsoidal forms as well, allowing a better representation of the clusters.

Keywords

calculus; fuzzy set theory; pattern clustering; possibility theory; statistical distributions; Euclidian distance; Gustafson-Kessel algorithm; Mahalanobis measure; calculus; cluster distribution; fuzzy membership degree; possibilistic fuzzy c-means clustering; Automation; Calculus; Clustering algorithms; Clustering methods; Data analysis; Equations; Particle measurements; Phase change materials; Prototypes; Telecommunications; Gustafson-Kessel clustering; c-means clustering; fuzzy clustering; possibilistic clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation Congress, 2006. WAC '06. World

Conference_Location

Budapest

Print_ISBN

1-889335-33-9

Type

conf

DOI

10.1109/WAC.2006.376056

Filename

4259972