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) |
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| 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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