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Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-25
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data.
In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed.
Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA).
The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Centers.
The real dataset of UCI was used to test the WKPCA algorithm.
Experimental results show that WKPCA algorithm is more efficient and robust than other k-prototypes algorithms.
American Psychological Association (APA)
Jia, Ziqi& Song, Ling. 2020. Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195710
Modern Language Association (MLA)
Jia, Ziqi& Song, Ling. Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1195710
American Medical Association (AMA)
Jia, Ziqi& Song, Ling. Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195710
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195710