Fuzzy c-ordered-means clustering

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 LpLp norm clustering (LpLp FCM) (0