A novel approach for fuzzy clustering based on neutrosophic association matrix. (January 2019) - Long, Hoang Viet; Ali, Mumtaz; Son, Le Hoang; Khan, Mohsin; Tu, Doan Ngoc
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.