%0 Journal Article %A DING Songyang %A TIAN Qingyun %T Density Peak Clustering Algorithm Based on Ball-Tree %D 2021 %R 10.3778/j.issn.1002-8331.2009-0284 %J Computer Engineering and Applications %P 90-96 %V 57 %N 20 %X

In order to overcome the deficiencies of clustering by fast search and find of density peaks(DPC) for its high time complexity and low accuracy, an optimized fast density peak clustering algorithm is proposed based on Ball-Tree in this paper(BT-DPC). The algorithm defines local density of a point based on [k]-nearest neighbor, and constructs a ball tree to accelerate the calculation of the local density [ρ] and the distance [δ]. In the cluster allocation stage, the statistical learning allocation strategy is designed based on the [k]-nearest neighbors idea to classify the boundary points correctly. The experimental result shows that the BT-DPC algorithm can improve the time performance on the basis of increasing clustering quality compared with DPC algorithm and other popular clustering algorithms through the theory analysis and the experiments on several real-world datasets from the UCI machine learning repository.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2009-0284