Density Based Clustering via Dominant Sets

Abstract

While density based clustering algorithms are able to detect clusters of arbitrary shapes, their clustering results usually rely heavily on some user-specified parameters. In order to solve this problem, in this paper we propose to combine dominant sets and density based clustering to obtain reliable clustering results. We firstly use the dominant sets algorithm and histogram equalization to generate the initial clusters, which are usually subsets of the real clusters. In the second step the initial clusters are extended with density based clustering algorithms, where the required parameters are determined based on the initial clusters. By merging the merits of both algorithms, our approach is able to generate clusters of arbitrary shapes without user-specified parameters as input. In experiments our algorithm is shown to perform better than or comparably to some state-of-the-art algorithms with careful parameter tuning.