Journal
PATTERN RECOGNITION
Volume 41, Issue 4, Pages 1384-1397Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.patcog.2007.08.014
Keywords
clustering; unsupervised learning; Dempster-Shafer theory; evidence theory; belief functions; cluster validity; robustness
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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. (c) 2007 Elsevier Ltd. All rights reserved.
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