Title :
Robust Extension of FCM Algorithm
Author_Institution :
Sch. of Sci., Hangzhou Dianzi Univ.
Abstract :
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
Keywords :
fuzzy set theory; minimisation; pattern clustering; FCM algorithm; clustering method; criterion function minimization; fuzzy c-means method; Clustering algorithms; Clustering methods; Cybernetics; Data engineering; Data mining; Electronic mail; Image processing; Machine learning; Machine learning algorithms; Minimization methods; Modeling; Noise reduction; Noise robustness; Pattern recognition; Fuzzy clustering; criterion function; fuzzy c-means;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258710
