Clustering Validity Evaluation Functions of Fuzzy C-means Clustering Algorithm.

Fuzzy C-means (FCM) clustering algorithm is a method mainly applied to machine learning and data mining. It can cluster objects into a limited number of categories according to their similarity degree without much prior knowledge. However, FCM clustering algorithm must first give a predefined number of clusters. Therefore, it is crucial to use clustering effectiveness function to get the optimal cluster number. Therefore, partition coefficient, partition entropy, separation index, Bensaid clustering validity function, Xie and Beni clustering validity function, Dunn clustering validity function and the improved Dunn clustering validity function were selected. Clustering experiments were conducted on three typical UCI data sets in view of FCM clustering algorithm. Finally, different fuzzy indexes are used to evaluate the validity of clustering. [ABSTRACT FROM AUTHOR]