An Improved Density Peak Clustering Algorithm Based on K Nearest Neighbors and Tissue-like P System

Recently, the density peak clustering algorithm (DPC) has attracted wide attention of researchers. DPC can quickly find the clustering centers and complete the clustering task. However, DPC still has some defects, such as the need to manually set the cutoff distance, the cascade reaction of points distribution, and the vulnerability to noise interference. In order to address these problems, we propose an improved density peak clustering algorithm based on K nearest neighbors and tissue-like P system. Firstly, the local density of each data point is calculated on the basis of K nearest neighbors and the clustering centers are selected via the Score value. Afterward, the remaining points are assigned according to the new similarity matrix calculated by KNN. Moreover, we embed the improved algorithm into the framework of the tissue-like P system, so that the maximum parallelism of the P system will improve the computational efficiency of the algorithm. The experimental results on multiple synthetic datasets and real datasets illustrate that the improved algorithm has a better clustering effect than other algorithms.