Cluster Identification Using Maximum Configuration Entropy

Abstract

Clustering is an important task in data mining and machine learning. In this paper, a normalized graph sampling algorithm for clustering that improves the solution of clustering via the incorporation of a priori constraint in a stochastic graph sampling procedure is adopted. The important question of how many clusters exists in the dataset and when to terminate the clustering algorithm is solved via computing the ensemble average change in entropy. Experimental results show the feasibility of the suggested approach.