Density Based Dominant Sets Growing and Clustering

Data clustering is an important technique in data mining and pattern recognition. In practical tasks the clusters can be of arbitrary shapes. However, many existing algorithms tend to generate only spherical clusters. While density based clustering algorithms are able to deal with arbitrary clusters, they usually involve multiple user-specified parameters. In this paper we propose to solve this problem by making use of the nice properties of dominant set algorithm. Specifically, we use the dominant sets algorithm with histogram equalization transformation to generate initial clusters. These initial clusters are usually subsets of real clusters. Then we expand the initial clusters to final ones with density information captured in the initial clusters. We experiment with clusters of various types and compare with other clustering algorithms to demonstrate the effectiveness of the proposed algorithm.