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Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy C-Ordered-Means (FCOM) clustering. This method uses both the Huber's M-estimators and the Yager's OWA operators to obtain its robustness. The proposed method is compared to many other ones, e.g.: the Fuzzy C-Means (FCM), the Possibilistic Clustering (PC), the fuzzy Noise Clustering Method (NCM), the L-p norm clustering (L-p FCM) (0 < p < 1), the L-1 norm clustering (L-1 FCM), the Fuzzy Clustering with Polynomial Fuzzifier (PFCM) and the epsilon-insensitive Fuzzy C-Means (beta FCM). To this end experiments on synthetic data with outliers have been performed as well as on data with heavy-tailed and overlapping groups of points in background noise. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Fuzzy clustering
Fuzzy c-means
epsilon-Insensitivity
Ordered weighted averaging
Robust methods
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