Merging DBSCAN and Density Peak for Robust Clustering

In data clustering, density based algorithms are well known for the ability of detecting clusters of arbitrary shapes. DBSCAN is a widely used density based clustering approach, and the recently proposed density peak algorithm has shown significant potential in experiments. However, the DBSCAN algorithm may misclassify border data points of small density as noises and does not work well with large density variance across clusters, and the density peak algorithm has a large dependence on the detected cluster centers. To circumvent these problems, we make a study of these two algorithms and find that they have some complementary properties. We then propose to combine these two algorithms to overcome their problems. Specifically, we use the DP algorithm to detect cluster centers and then determine the parameters for DBSCAN adaptively. After DBSCAN clustering, we further use the DP algorithm to include border data points of small density into clusters. By combining the complementary properties of these two algorithms, we manage to relieve the problems of DBSCAN and avoid the drawbacks of the density peak algorithm in the meanwhile. Our algorithm is tested with synthetic and real datasets, and is demonstrated to perform better than DBSCAN and density peak algorithms, as well as some other clustering algorithms.