A novel approach for fuzzy clustering based on neutrosophic association matrix. (January 2019)

• Record Type: Journal Article
• Authors: Long, Hoang Viet; Ali, Mumtaz; Son, Le Hoang; Khan, Mohsin; Tu, Doan Ngoc
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 687
• Page End: 697
• Publication Date: 2019-01
• Subjects: Fuzzy clustering -- Neutrosophic set -- Association matrix -- Lambda-cutting matrix -- Clustering quality; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.11.007
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We proposed a new fuzzy clustering algorithm based on the neutrosophic set. Data are fuzzified to create neutrosophic association and equivalence matrix. Lambda-cutting matrix is used to determine the clusters. It was experimentally validated on benchmark datasets of UCI Machine Learning. It has better clustering quality than other relevant algorithms. 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.