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To solve both the similarity calculation method and parameter limits problems of the affinity propagation algorithm (AP), the self-adaptive affinity propagation clustering algorithm based on density peak clustering and weighted similarity (DPWSAP) was proposed. The solutions were following: 1) density peak algorithm (DP) was introduced to create the local density attribute for AP algorithm; 2) weighted similarity was applied to heighten the similarity extent of data points; 3) growth curve function model was employed with setting a self-adaptive strategy for damping factor ( $\lambda$ ) to enhance the convergence performance of AP at different stages. To verify the performance of DPWSAP we tested six UCI data sets with different density, different dimensions, and data volume. Experimental results indicated that DPWSAP had better clustering accuracy and convergence performance than original AP algorithm and several other clustering algorithms. In addition, the self-adaptive strategy improved the overall performance for the algorithm, and reduced the possibility of human factors affecting the algorithm effect. The analysis results demonstrated that the DPWSAP had a good research value. Thus, the proposed algorithm had a better research prospect in theory and application fields.

A Novel Self-Adaptive Affinity Propagation Clustering Algorithm Based on Density Peak Theory and Weighted Similarity