REFINED NEUTROSOPHIC HIERARCHICAL CLUSTERING METHODS

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Published: 2017-03-02

Page: 283-295


MEHMET ŞAHIN

Department of Mathematics, Gaziantep University, Gaziantep 27310, Turkey.

ORHAN ECEMIŞ

Department of Computer Technology, Gaziantep University, Gaziantep 27310, Turkey.

VAKKAS ULUÇAY

Department of Mathematics, Gaziantep University, Gaziantep 27310, Turkey.

HARUN DENIZ

Department of Mathematics, Gaziantep University, Gaziantep 27310, Turkey.

*Author to whom correspondence should be addressed.


Abstract

Clustering plays an important role in data mining, pattern recognition, and machine learning. In recent times, refined neutrosophic logic, set, and probability were introduced by Smarandache in 2013 [1] and later used by Deli in 2016 [2] has been one of the most powerful and flexible approaches for dealing with complex and uncertain situations of real world. We propose a hierarchical clustering method using distance-based similarity measures on refined neutrosophic sets. Then, we present a clustering algorithm based on the similarity measures of refined neutrosophic sets to cluster refined neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.

Keywords: Neutrosophic sets, refined neutrosophic sets, similarity measure, decision making


How to Cite

ŞAHIN, M., ECEMIŞ, O., ULUÇAY, V., & DENIZ, H. (2017). REFINED NEUTROSOPHIC HIERARCHICAL CLUSTERING METHODS. Asian Journal of Mathematics and Computer Research, 15(4), 283–295. Retrieved from https://ikprress.org/index.php/AJOMCOR/article/view/798

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