L-DP: A hybrid density peaks clustering method

Abstract

Density peaks (DP) clustering is a new density-based clustering method. This algorithm can deal with some data sets having non-convex clusters. However, when the shape of clusters is very complicated, it cannot find the optimal structure of clusters. In other words, it cannot discover arbitrary shaped clusters. In order to solve this problem, a new hybrid clustering method, called L-DP, is proposed in this paper combines density peaks clustering with the leader clustering method. Experiments on synthetic datasets show L-DP could be a suitable one for arbitrary shaped clusters compared with the original DP clustering method. The experimental results on real-world data sets demonstrate that the proposed algorithm is competitive with the state-of-the-art clustering algorithms, such as DP, AP and DBSCAN.