A Parameter-Independent Clustering Framework

Abstract

The existing clustering algorithms are usually dependent on one or more input parameters, which are not easy to determine in many cases. In this paper, DSets-histeq is presented as a parameter-independent framework for data clustering. By histogram equalization transformation of pairwise data similarity matrices, the dominant sets algorithm is used to generate parameter-independent initial clusters, which are typically relatively large subsets of real clusters. This enables one to expand the initial clusters to the final ones with a cluster-growing algorithm and determine the involved parameters adaptively by making use of the information in the initial clusters. A simple yet effective method is proposed to utilize the information captured in the initial clusters. Experiments on various datasets and comparison with state-of-the-art clustering algorithms are used to illustrate the potential of the proposed framework.