期刊
PATTERN RECOGNITION
卷 41, 期 4, 页码 1384-1397出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.patcog.2007.08.014
关键词
clustering; unsupervised learning; Dempster-Shafer theory; evidence theory; belief functions; cluster validity; robustness
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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