%0 Journal Article
%A GAO Yue
%A YANG Xiaofei
%A MA Yingcang
%A WANG Yirui
%T Density Peak Clustering Based on Shared [k]-Nearest Neighbors and Shared Reverse Nearest Neighbors
%D 2019
%R 10.3778/j.issn.1002-8331.1903-0246
%J Computer Engineering and Applications
%P 43-51
%V 55
%N 20
%X 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.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1903-0246