关键词: 密度峰值聚类算法, 鲸鱼优化算法, 聚类中心自适应, 截断距离

Abstract:

To solve these problems of the Density Peaks Clustering algorithm（DPC） that the clustering results are more sensitive to the cutoff distance [dc], as well as the clustering centers selected manually are subjective, an adaptive Density Peaks Clustering algorithm combining with Whale Optimization Algorithm（WOA-DPC） is proposed. The selection of clustering center is automatically realized according to the slope variation trend of the weighted product of the local density and the relative distance, which avoids the situation that the number of the clustering centers selected by manual operation is larger or smaller. The reasonable cutoff distance [dc] is an important factor to improve the clustering result of DPC. An optimization problem with the objective function being the ACC index is established. The objective function is optimized by using the effective optimization ability of the Whale Optimization Algorithm（WOA） to find the best cutoff distance [dc]. The proposed WOA-DPC is tested with the artifical datasets and the real datasets on UCI. Experimental results show that the proposed algorithm outperforms DPC, DBSCAN and K-Means in terms of FMI, ARI and AMI indicators with better clustering performance.

Key words: density peak clustering algorithm, whale optimization algorithm, cluster center adaptive, cutoff distance