Natural neighbor-based clustering algorithm with density peeks

Clustering analysis has been widely used in many areas such as astronomy, bioinformatics, and pattern recognition. In 2014, Rodriguez proposed an algorithm based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher density. But the density relies on cutoff distance, which might be affected by large statistical error, and the algorithm does not suit the clustering problem of multi-scale data. In this paper, a new neighbor concept Natural Neighbor is proposed. Natural neighbor-based density, is simple and well reflects the data distribution without any parameters. Then, we extend each cluster from its center by searching natural neighbors of points in this cluster, and we define extension rules to determine the cluster boundary. The experiment results show our algorithm is more effective on multi-scale data.