关键词: 聚类, 密度峰值, 自动选择, 类中心点

Abstract:

Density Peak Clustering（DPC） is a clustering method based on local density. There are two basic factors in the DPC that affect the effect of the algorithm：local density definition and class center selection. For the classical DPC, the specific distribution of sample points in the neighborhood is not taken into consideration when defining the local density, and the cluster center cannot be automatically selected in the cluster. A local density definition based on distribution and an automatic class center selection strategy based on the maximum classes’ square error method are proposed. Firstly, the number of data points within the circle of each sample point is calculated, and the distribution of data points is considered. When there are the same number of points in the circle, if the distribution of data points in the circle is more uniform, the local density of the point is larger, and the probability of peak density is higher. The class center is then found by automatically selecting the threshold by the maximum classes’ square error method（Otsu）. Experimental results show that the new algorithm can not only automatically select the clustering center, but also obtain higher classification accuracy than the existing original algorithm.

Key words: clustering, density peak, automatic selection, class midpoint