As an effective clustering method, Affinity Propagation (AP) has received extensive attentions. But its applications are seriously limited by two major deficiencies. Firstly, the ultimate exemplars and clusters are sensitive to a list of user-defined parameters called preferences. Secondly, it cannot deal with the nonspherical cluster issue. To solve these problems, an adaptive density distribution inspired AP clustering algorithm is proposed in this work. Aiming at the difficulties in preference selection, a density-adaptive preference estimation algorithm is proposed to explore the underlying exemplars, which can obtain the better clustering results by only using the local density distributions of data. Aiming at the arbitrary shape cluster problem, a non-parameter similarity measurement strategy based on the nearest neighbor searching is presented to describe the true structures of data, and then, the data with both spherical and nonspherical distributions can be clustered. The experiments conducted on various synthetic and public datasets demonstrate that the proposed method outperforms other state-of-the-art approaches.
Adaptive clustering Affinity propagation Density distribution Nearest neighbor network
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