Extension of fuzzy c-means algorithm
Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity. The fuzzy c-means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, the FCM method is sensitive to the presence of noise and outliers in data. This paper introduces a new clustering algorithm by extending the criterion function. As a special case, this algorithm includes the well-known fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with the FCM method using synthetic data with different clusters and outliers.