Similarity Measures of Pythagorean Neutrosophic Sets with Dependent Neutrosophic Components Between T and F

Abstract

Clustering plays an important role in data mining, pattern recognition and machine learning. This paper proposes Pythagorean neutrosophic clustering methods based on similarity measures between Pythagorean neutrosophic sets with T and F are dependent neutrosophic components [PN-Set]. First, we define a generalized distance measure between PN-Sets and propose two distance-based similarity measures of PN-Sets. Then, we present a clustering algorithm based on the similarity measures of PN-Sets to cluster Pythagorean neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.