Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient. (25th July 2020)

• Record Type: Journal Article
• Authors: Jia, Ziqi; Song, Ling
• Other Names: Sadarangani Kishin Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-07-25
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/5143797
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.