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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,...
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...
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...
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