A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. (31st July 2019)

• Record Type: Journal Article
• Authors: Jiang, Zhenni; Liu, Xiyu; Sun, Minghe
• Other Names: Spagnolo Paolo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2019(2019)
• Publication Date: 2019-07-31
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2019/1713801
• Languages: English
• ISSNs: 1024-123X

Abstract

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.