Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”
Joint Authors
Min, Xiangqiang
Huang, Yi
Sheng, Yehua
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining.
Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means.
Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP.
CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks.
But with CFSFDP, the essential second step of finding clustering centers must be done manually.
Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply.
To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention.
First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method.
Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers.
Finally, the remaining objects are grouped into clusters.
This algorithm can efficiently and automatically determine clustering centers without calculating additional variables.
We verified ACDPC using three standard datasets and compared it with other clustering algorithms.
The experimental results show that ACDPC is more efficient and robust than alternative methods.
American Psychological Association (APA)
Min, Xiangqiang& Huang, Yi& Sheng, Yehua. 2020. Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1195365
Modern Language Association (MLA)
Min, Xiangqiang…[et al.]. Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1195365
American Medical Association (AMA)
Min, Xiangqiang& Huang, Yi& Sheng, Yehua. Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1195365
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195365