WANN-DPC: Density peaks finding clustering based on Weighted Adaptive Nearest Neighbors

DPC (Density Peak Clustering) algorithm and most of its variants are unable to identify the cluster centers of dense and sparse clusters simultaneously. In addition, the “Domino Effect” of DPC cannot be entirely avoided in its variants. Despite ANN-DPC (Adaptive Nearest Neighbor DPC) being able to detect cluster centers of dense and sparse clusters, its adaptive nearest neighbors of a point may introduce bias in the local density, cluster centers and clustering. To address these limitations of ANN-DPC, the WANN-DPC (Weighted Adaptive Nearest Neighbor DPC) algorithm is proposed. The key contributions of WANN-DPC are as follows: (1) A novel weighted local density of a point is defined by weighting its close and far neighbors, (2) a correction factor is proposed to detect cluster centers in turn, and (3) a two-step assignment strategy is presented utilizing nearest neighbor relationships and weighted membership degrees. Extensive experiments on benchmark datasets demonstrate the superiority of the WANN-DPC over its peers.