Merging dominant sets and DBSCAN for robust clustering and image segmentation

Dominant sets clustering is a promising clustering approach based on a graph-theoretic concept of a cluster. With the pairwise similarity matrix of data as input, dominant sets clustering determines the number of clusters by itself and possesses some other nice properties. However, the original dominant sets clustering algorithm is sensitive to similarity measures, and appropriate parameters are required to generate satisfactory clustering results. In order to solve this problem, we firstly use histogram equalization to transform the similarity matrix and remove the sensitiveness to similarity parameters. In the second step we extend the clusters by merging dominant sets clustering and DBSCAN. Our algorithm requires no user-defined parameters, and is able to generate clusters of arbitrary shapes and determine the number of clusters automatically. In experiments of data clustering and image segmentation our algorithm performs evidently better than the original dominant sets clustering, and also comparably to other state-of-the-art clustering algorithms.