关键词: 密度峰聚类, 共享[k]-近邻与共享逆近邻, 共享相似度, 共享密度

Abstract: In order to better solve the problem of density imbalance and characterize the similarity measure of high-dimensional data, a density peak clustering algorithm based on shared [k]-nearest neighbors and shared reverse nearest neighbors is proposed. This algorithm first calculates the shared [k]-nearest neighbor number and the shared reverse nearest neighbor number of two points, and combines them with the Euclidean distance to determine the shared similarity between the two points. In the following it defines shared density of a point by sum of shared similarities between this point and its reverse nearest neighbors, and then selects the cluster center by the shared density. The experimental results show that the clustering results of the algorithm on the artificial dataset and the real dataset are more accurate than other density clustering algorithms. So the algorithm can better deal with the density imbalance problem, and also improves the clustering accuracy of high-dimensional data.

Key words: density peak clustering, shared [k]-nearest neighbors and shared reverse nearest neighbors, shared similarity, shared density