Enhancing Density Peak Clustering via Density Normalization

Clustering is able to find out implicit data distribution and is especially useful in data driven machine learning. Density based clustering has an attractive property of detecting clusters of arbitrary structures. The density peak algorithm makes use of two assumptions to detect cluster centers and then groups the other data. This approach is simple to implement and shown to be promising in many experiments. However, we find its clustering results are dependent on density kernel types and kernel parameters, and density difference across clusters also influences the results significantly. We make a detailed study of the density peak algorithm and attribute the problems to the local density criterion in detecting cluster centers. We then use density normalization to relieve the influence of the problems, and present a density kernel to further improve clustering results. We conduct experiments with different types of datasets to demonstrate the performance of our approach.