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Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph
Joint Authors
Yaping, Dai
Diao, Qi
An, Qichao
Li, Weixing
Feng, Xiaoxue
Pan, Feng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-12
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes.
Most of the conventional clustering approaches work only with round-shaped clusters.
This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy.
To overcome these limitations, two improvements are proposed in this paper.
To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN).
A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph.
Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out.
Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI).
American Psychological Association (APA)
Diao, Qi& Yaping, Dai& An, Qichao& Li, Weixing& Feng, Xiaoxue& Pan, Feng. 2020. Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph. Complexity،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1139949
Modern Language Association (MLA)
Diao, Qi…[et al.]. Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph. Complexity No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1139949
American Medical Association (AMA)
Diao, Qi& Yaping, Dai& An, Qichao& Li, Weixing& Feng, Xiaoxue& Pan, Feng. Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph. Complexity. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1139949
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139949