ECM: An evidential version of the fuzzy \(c\)-means algorithm. (English) Zbl 1131.68081
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.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T10 | Pattern recognition, speech recognition |
Keywords:
clustering; unsupervised learning; Dempster-Shafer theory; evidence theory; belief functions; cluster validity; robustnessSoftware:
evclustReferences:
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