An enhanced dominant sets clustering method
Jian Hou, Weixue Liu, Hongxia Cui · 2016
As a graph-based clustering approach, dominant sets clustering determines the number of clusters automatically and possesses some other nice properties. By applying histogram equalization transformation to the similarity matrix before clustering, we are able to accomplish the dominant sets clustering process without any user-specified parameters. However, this transformation usually leads to over-segmented clustering results. In this paper, we analyze the correlation between histogram equalization transformation and the over-segmentation tendency, and attribute the over-segmentation to the over-strict global density constraint imposed by the dominant set definition. Therefore we propose to relax the global density constraint to a local one, which is then used in dominant set extension. We test our algorithm in experiments of data clustering and image segmentation, and validate its effectiveness in comparison with the original dominant sets algorithm and other algorithms.