ECM: An evidential version of the fuzzy c-means algorithm

A new clustering method for object data, called ECM (evidential c-means) is introduced, in the theoretical framework of belief functions. It is based on the concept of credal partition, extending those of hard, fuzzy, and possibilistic ones. To derive such a structure, a suitable objective function...

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Bibliographic Details
Published in:Pattern recognition Vol. 41; no. 4; pp. 1384 - 1397
Main Authors: Masson, Marie-Hélène, Denœux, T.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.04.2008
Elsevier Science
Subjects:
Applied sciences
Belief functions
Cluster validity
Clustering
Dempster–Shafer theory
Evidence theory
Exact sciences and technology
Information, signal and communications theory
Robustness
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Unsupervised learning
Belief functions
Cluster validity
Evidence theory
Dempster–Shafer theory
Robustness
Clustering
Unsupervised learning
Automatic classification
Belief function
Dempster-Shafer theory
Signal classification
Credal approach
Fuzzy algorithm
Dempster Shafer theory
Objective function
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:A new clustering method for object data, called ECM (evidential c-means) is introduced, in the theoretical framework of belief functions. It is based on the concept of credal partition, extending those of hard, fuzzy, and possibilistic ones. To derive such a structure, a suitable objective function is minimized using an FCM-like algorithm. A validity index allowing the determination of the proper number of clusters is also proposed. Experiments with synthetic and real data sets show that the proposed algorithm can be considered as a promising tool in the field of exploratory statistics.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2007.08.014