A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System
Joint Authors
Liu, Xiyu
Sun, Minghe
Jiang, Zhenni
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-31
Country of Publication
Egypt
No. of Pages
13
Main Subjects
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.
American Psychological Association (APA)
Jiang, Zhenni& Liu, Xiyu& Sun, Minghe. 2019. A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194493
Modern Language Association (MLA)
Jiang, Zhenni…[et al.]. A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1194493
American Medical Association (AMA)
Jiang, Zhenni& Liu, Xiyu& Sun, Minghe. A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1194493
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194493