A New Algorithm for Clustering Analysis

In this paper, a new algorithm for unsupervised Clustering analysis is proposed, through a new kind of iterative activation the examples of a cluster are moved inside to the center of the gravity of the cluster together. Through this method correct number of clusters could be got. Because each sample moves only in its own cluster while iterating, we can correctly tell which cluster a sample should belong to. The experiments show that the new algorithm has better results in several aspects than HCM and FCM algorithms, such as unsupervised clustering, correct clustering, clustering capability for special data which HCM and FCM algorithms can not cluster. The new algorithm is an unsupervised clustering algorithm but HCM and FCM algorithms need correct number of clusters before iterative activation.