A novel approach for fuzzy clustering based on neutrosophic association matrix

•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 be...

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Bibliographic Details
Published in:Computers & industrial engineering 2019-01, Vol.127, p.687-697
Main Authors: Long, Hoang Viet, Ali, Mumtaz, Son, Le Hoang, Khan, Mohsin, Tu, Doan Ngoc
Format: Article
Language:English
Subjects:
Association matrix
Clustering quality
Fuzzy clustering
Lambda-cutting matrix
Neutrosophic set
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Summary:•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. 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.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2018.11.007