A new density kernel in density peak based clustering

Jian Hou, Marcello Pelillo

Abstract: The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness of cluster centers by making use of the farthest ones in nearest neighbors of data. Furthermore, we propose to use density normalization to deal with the density difference among clusters. Given the number of clusters, our algorithm is able to accomplish the clustering process without human intervention and improve the clustering results. In experiments on several datasets, our algorithm is shown to outperform the original one with both cutoff and Gaussian kernels evidently.