ECM: An evidential version of the fuzzy c-means algorithm
نویسنده
چکیده
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 a 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.
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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 a FCM-like algorithm. A validity index allowing the determination of the proper...
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