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

Date

2019-07-31

Authors

Jiang, Zhenni
Liu, Xiyu
Sun, Minghe

Journal Title

Journal ISSN

Volume Title

Publisher

Hindawi

Abstract

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.

Description

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Citation

Jiang, Z., Liu, X., & Sun, M. (2019). A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. Mathematical Problems in Engineering, 2019, 1713801. doi:10.1155/2019/1713801

Department

Management Science and Statistics