REFINED NEUTROSOPHIC HIERARCHICAL CLUSTERING METHODS

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.