An improved density peaks clustering algorithm based on the generalized neighbors similarity

Density peaks clustering (DPC) algorithm reported in Science is a novel and efficient clustering method which has attracted great attention for its simplicity and practicability. Although it has shown promising results in some applications, there still exist some certain disadvantages. For example, the calculation method of local density without taking into account the impact of the surrounding areas may cause the wrong cluster centers selection results. In spite of the simple data points allocation strategy, the allocation strategy may cause the serial incorrect cluster results. Given these disadvantages of DPC algorithm, we propose a nearest neighbors similarity based clustering method which is called generalized neighbors similarity based clustering by fast search and find of density peaks (abbreviated as GNS-DPC). Considering the data points' K-nearest-neighbor information, we give a generalized neighbors similarity measurement between data points and present a new definition of local density and relative distance. In the data points allocation stage, this GNS-DPC also takes advantage of the nearest neighbors'information of a data point. The allocation process consists of several steps, which can solve the serial incorrect cluster results problem. The experimental results suggest that our method can correctly obtain the cluster centers and recognize clusters with higher accuracy.