Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue—Like P System

Density Peaks Clustering (DPC) has recently received much attention in many fields by reason of its simplicity and efficiency. Nevertheless, empirical studies have shown that DPC has some shortfalls: (i) similarity measurement based on Euclidean distance is prone to misclassification. When dealing with clusters of non-uniform density, it is very difficult to identify true clustering centers in the decision graph; (ii) the clustering centers need to be manually selected; (iii) the chain reaction; an incorrectly assigned point will affect the clustering outcome. To settle the above limitations, we propose an improved density peaks clustering algorithm based on a divergence distance and tissue—like P system (TP-DSDPC in short). In the proposed algorithm, a novel distance measure is introduced to accurately estimate the local density and relative distance of each point. Then, clustering centers are automatically selected by the score value. A tissue—like P system carries out the entire algorithm process. In terms of the three evaluation metrics, the improved algorithm outperforms the other comparison algorithms using multiple synthetic and real-world datasets