关键词: 密度聚类, 快速搜索与发现密度峰值聚类（DPC）, 分配策略

Abstract:

Focused on the issue that density peaks clustering algorithm will make mistakes when facing data sets allocation with complex structures, a kind of density peaks clustering with optimized allocation strategy (ODPC) is proposed in this paper. Firstly, the parameter product [γ] is introduced into the new algorithm to expand the selection of cluster centers. Then, it proposes an improved allocation strategy for data points, which redistributes points of data sets with similarity index [MS,] and further optimizes the allocation of points. Finally, [dc] nearest neighbor method is used to optimally identify the noise points of data sets. The experiments on artificial and UCI real data sets show that the new algorithm can improve the accuracy of complex manifold data sets allocation while opti-mizing noise recognition, and achieves better clustering results than DPC (clustering by fast search and find of den-sity peaks), DenPEHC (density peak based efficient hierarchical clustering) and GDPC (density peaks clustering algorithm with gird-division strategy) algorithms.

Key words: density clustering, clustering by fast search and find of density peaks (DPC), allocation strategy