Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders
Joint Authors
Ding, Lin
Chen, Yuantao
Xu, Weihong
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-18
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise.
However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers.
In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders.
DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs).
And then, subclusters are merged into individuals by comparing the density of edge samples.
By iteration of the merger, the suitable dc and cluster centers are ensured.
Finally, we compared state-of-the-art methods with our proposal in public datasets.
Experiments show that our algorithm automatically determines cutoff distance and centers accurately.
American Psychological Association (APA)
Ding, Lin& Xu, Weihong& Chen, Yuantao. 2020. Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138955
Modern Language Association (MLA)
Ding, Lin…[et al.]. Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1138955
American Medical Association (AMA)
Ding, Lin& Xu, Weihong& Chen, Yuantao. Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138955
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138955