关键词: 聚类算法, 密度峰值, K近邻, 决策图, 类簇中心

Abstract: The clustering effect of the Fast Search and Discovery Density Peak Clustering Algorithm（CFSFDP） relies heavily on the subjective setting of the truncation distance [dc], while the determination of the optimum value is not easy, and when dealing with the data sets with complex structure and large variations in density, the distinction generated by CFSFDP algorithm between the cluster center points and the non-cluster center points in the decision graph is not obvious enough, making the selection of the cluster centers difficult. Aiming at these problems, the algorithm is optimized and a Comparative Density Peak Clustering algorithm based on K-Nearest Neighbors（CDPC-KNN） is proposed. The algorithm combines the concept of K-nearest neighbors to redefine the measurement method of truncation distance and local density. It can adaptively generate the truncation distance for arbitrary datasets, and make the calculation results of local density more consistent with the real distribution of data. Meanwhile, the distance comparison quantity is introduced to replace the distance parameter, so that the cluster centers are more obvious on the decision graph. The experimental results show that the clustering effect of CDPC-KNN algorithm is better than CFSFDP algorithm and DBSCAN algorithm in general. The separation experiment shows that the new algorithm effectively improves the discrimination between cluster center points and non-cluster center points.

Key words: clustering algorithm, density peaks, K-nearest neighbors, decision graph, cluster centers