A Centerness Peak Based Clustering Algorithm

The density peak based clustering algorithm is a recently proposed density based clustering approach. This algorithm treats the data corresponding to local density peaks as cluster centers and groups non-center data based on the density relationship among neighboring data. While being simple, this algorithm is shown to be effective and computationally efficient. In the density peak based algorithm, the data with the largest local density are selected as cluster centers. On one hand, the real cluster centers may not have the largest local density in clusters. On the other hand, this practice may discriminate against the clusters of small density. In this paper we propose to measure the centerness of data and treat the centerness peaks as cluster centers. The centerness of one data is evaluated by the distribution of nearest neighbors in the neighborhood, and it measures to which degree one data is surrounded by its nearest neighbors. We present a histogram based method to calculate the centerness, and show that the centerness measure solves the problem resulted from density difference among clusters in the density peak based algorithm. In addition, the cluster centers identified by centerness peaks are more consistent with human observation. Experiments on various datasets and comparisons with other algorithms illustrate the effectiveness of our algorithm.