关键词: 密度峰值聚类算法, 聚类分析, 引力定律, 局部密度, 第一宇宙速度

Abstract:

Aiming at the problem that the Density Peak Clustering（DPC） algorithm randomly selects the truncation distance and assigns the residual error rate of the residual sample points when calculating the local density of the sample, a physics improved density peak clustering algorithmW-DPC is proposed. Firstly, the local density of the sample is defined by the law of universal gravitation; then, a two-step strategy is established based on the first cosmic velocity to allocate the remaining sample points：that is, it must belong to the distribution of points and may belong to the distribution of points, so that the distribution of remaining sample points is more accurately. Finally, the W-DPC algorithm is tested with the artificial dataset and the real dataset on UCI, and compared with KNN-DPC algorithm, DPC algorithm, DBSCAN algorithm, AP algorithm and K-Means algorithm. The results show that the clustering effect of the W-DPC algorithm is significantly better than other algorithms.

Key words: density peak clustering algorithm, cluster analysis, gravitational theory, local density, first cosmic speed