A density-peak-based clustering algorithm of automatically determining the number of clusters

Abstract

Clustering is a typical and important method to discover new structures and knowledge from data sets. Most existing clustering methods need to know the number of clusters in advance, which is difficult. Some algorithms claim they do not need to know the number of clusters in advance. Among these algorithms, however, some need to manually determine the cluster centers in a decision graph, which is not easy; some assume that the number of initial cluster centers given is greater than the actual number of classes, but in fact the true number of clusters is not known. In order to tackle this issue, we propose a density-peak-based clustering algorithm of automatically determining the number of clusters. First, we design a density metric by using a continuous function which can well distinguish the densities of different data points. Then, we design a pre-clustering method which can get the initial cluster centers and the corresponding clusters. Furthermore, we propose an automatic clustering method which can automatically determine the final cluster centers and the corresponding clusters. Experiments are conducted on widely used data sets, and the results show the effectiveness of the proposed method.