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.