A novel approach for fuzzy clustering based on neutrosophic association matrix

Long, Hoang Viet and Ali, Mumtaz and Son, Le Hoang and Khan, Mohsin and Tu, Doan Ngoc (2018) A novel approach for fuzzy clustering based on neutrosophic association matrix. Computers and Industrial Engineering. ISSN 0360-8352

Abstract

This paper proposes a fuzzy clustering algorithm through neutrosophic association matrix. In the first step, data are fuzzified into neutrosophic sets to create neutrosophic association matrix. By deriving a finite sequence of neutrosophic association matrices, the neutrosophic equivalence matrix is generated. Finally, the lambda-cutting is performed over the neutrosophic equivalence matrix to derive the final lambda-cutting matrix which is used to determine the clusters. Experimental results on several benchmark datasets using different clustering criteria show the advantage of the proposed clustering over the existing algorithms.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 8 November 2018. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 13 Feb 2019 04:47
Last Modified: 14 Feb 2019 02:35
Uncontrolled Keywords: fuzzy clustering; neutrosophic set; association matrix; lambda-cutting matrix; clustering quality
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Identification Number or DOI: 10.1016/j.cie.2018.11.007
URI: http://eprints.usq.edu.au/id/eprint/35460

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