Robust Extension of FCM Algorithm
Cheng-Jia Li · 2006
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. A new clustering algorithm is proposed by extending the criterion function, which includes the well-known fuzzy c-means method as its special case. Numerical experiments show that the new clustering algorithm is less sensitive than the traditional FCM method and robust to outliers