The possibilistic C-means algorithm: insights and recommendations
Authors:
- 1 August 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Fuzzy Systems
- Vol. 4 (3) , 385-393
Abstract
Recently, the possibilistic C-means algorithm (PCM) was proposed to address the drawbacks associated with the constrained memberships used in algorithms such as the fuzzy C-means (FCM). In this issue, Barni et al. (1996) report a difficulty they faced while applying the PCM, and note that it exhibits an undesirable tendency to converge to coincidental clusters. The purpose of this paper is not just to address the issues raised by Barni et al., but to go further and analytically examines the underlying principles of the PCM and the possibilistic approach, in general. We analyze the data sets used by Barni et al. and interpret the results reported by them in the light of our findings.Keywords
This publication has 10 references indexed in Scilit:
- Comments on "A possibilistic approach to clustering"IEEE Transactions on Fuzzy Systems, 1996
- Estimation of Membership Functions for Pattern Recognition and Computer Vision1995
- Generation of membership functions via possibilistic clustering1994
- A least biased fuzzy clustering methodIEEE Transactions on Pattern Analysis and Machine Intelligence, 1994
- A possibilistic approach to clusteringIEEE Transactions on Fuzzy Systems, 1993
- Fitting an unknown number of lines and planes to image data through compatible cluster mergingPattern Recognition, 1992
- Robust Regression and Outlier Detection1987
- Robust Statistics1981
- Pattern Recognition with Fuzzy Objective Function Algorithms1981
- Fuzzy clustering with a fuzzy covariance matrix1978