An adaptive affinity propagation document clustering

The standard affinity propagation clustering algorithm suffers from one limitation that it is hard to know the value of the parameter “preference” which can yield an optimal clustering solution. To overcome this limitation, in this paper we proposes an adaptive affinity propagation method. The method first finds out the range of “preference”, then searches the space of “preference” to find a good value which can optimize the clustering result. We apply the method to document clustering and compare it with the standard affinity propagation and K-Means clustering method in real data sets. Experimental results show that our proposed method can get better clustering result.