A new clustering validity function for the Fuzzy C-means algorithm
Jie‐Sheng Wang · 2008
Fuzzy C-means (FCM) clustering algorithm is the unsupervised extraction of groups from an unlabelled data set with no prior knowledge of the underlying data structure. However there is a major limitation that exists in this method. A predefined number of clusters must be given in advance. In this paper, we propose a new validity index to deal with this situation. The performance evaluation of the proposed cluster validity index compares favorably with that of several validity functions and shows the effectiveness.