关键词: 数据挖掘, 聚类算法, 局部密度, 密度峰值, K近邻

Abstract: Concerning the problem that Density Peaks Clustering（DPC） algorithm has poor performance on the datasets with high dimension, noise and complex structure, an Improved Density Peaks Clustering Algorithm（IDPCA） combining K-Nearest Neighbors is proposed. Firstly, a new definition of local density is proposed to describe the distribution of the spatial samples. Secondly, the concept of core point is introduced and a global search allocation strategy is designed based on K-Nearest Neighbors thought to classify the unassigned K-Nearest Neighbors of core points correctly, which accelerates the clustering speed. Thirdly, a statistical learning allocation strategy is developed, by using the weighted K-Nearest Neighbors’ information of the unassigned points to calculate the probability of them being assigned to each local cluster, which improves the clustering quality effectively. Finally, compared with DPC and other three classical clustering methods on 21 test datasets including synthetic and real-world datasets, the experimental results show that IDPCA outperforms them on four different evaluation indexes.

Key words: data mining, clustering algorithm, local density, density peaks, K-Nearest Neighbors