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Density Peak Clustering Based on Relative Density Optimization
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-11
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Among numerous clustering algorithms, clustering by fast search and find of density peaks (DPC) is favoured because it is less affected by shapes and density structures of the data set.
However, DPC still shows some limitations in clustering of data set with heterogeneity clusters and easily makes mistakes in assignment of remaining points.
The new algorithm, density peak clustering based on relative density optimization (RDO-DPC), is proposed to settle these problems and try obtaining better results.
With the help of neighborhood information of sample points, the proposed algorithm defines relative density of the sample data and searches and recognizes density peaks of the nonhomogeneous distribution as cluster centers.
A new assignment strategy is proposed to solve the abundance classification problem.
The experiments on synthetic and real data sets show good performance of the proposed algorithm.
American Psychological Association (APA)
Li, Chunzhong& Zhang, Yunong. 2020. Density Peak Clustering Based on Relative Density Optimization. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1194049
Modern Language Association (MLA)
Li, Chunzhong& Zhang, Yunong. Density Peak Clustering Based on Relative Density Optimization. Mathematical Problems in Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1194049
American Medical Association (AMA)
Li, Chunzhong& Zhang, Yunong. Density Peak Clustering Based on Relative Density Optimization. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1194049
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194049