Density clustering with divergence distance and automatic center selection

The density peak clustering (DPC) algorithm is a famous density-based method for exploring, analyzing, and deriving information from data. In the case of application, it encounters the following bottlenecks. Firstly, its Euclidean distance based similarity measurement is prone to misclassification of neighbors. Secondly, its clustering results are significantly influenced by human factors (controlling the cut-off distance parameter dc and selecting the clustering center manually). Finally, the local density ρ is affected by parameter dc and cannot reflect the sparseness of the data distribution. To overcome these deficiencies, a novel density clustering algorithm, called NAPC, is proposed. First, a Divergence distance is defined to evaluate the similarity of points under the refined Euclidean distance space. Second, the specific value of dc is obtained based on the theory of Adjusted Boxplot. Then, the local density ρ is reinterpreted based on the above Divergence distance and newly assigned dc. Finally, a judgement index is given to determine which points can be regarded as the center points in the Divergence distance space. The performance of our proposal is evaluated by comparing with several existing clustering methods on various datasets. Experimental results prove that NAPC performs much better than several comparison algorithms. It can identify clusters of various shapes and spatial dimensions with minimal human intervention.