A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

Abstract EN

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy.

A variant of tissue-like P systems with active membranes is introduced to realize the clustering process.

The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity.

Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.