Single valued neutrosophic minimum spanning tree and its clustering method

Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single value neutrosophic information, the paper proposes a single valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present a SVNMST clustering algorithm for clustering single value neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach