INTERVAL NEUTROSOPHIC MST CLUSTERING ALGORITHM AND ITS AN APPLICATION TO TAXONOMY

Interval neutrosophic sets (INSs) are a generalization of interval valued intuitionistic fuzzy sets (IVIFSs) whose the membership and non-membership values of elements consist of interval values. In this paper, we extend the clustering techniques based graph theory given for IVIFSs to INSs. Firstly, we propose a minimal spaning tree (MST) algorithm based on distance measure under interval neutrosophic environment and then use the interval neutrosophic MST (INMST) clustering algorithm to classify any dataset in here. Finally, we present some numerical examples in order to demonstrate the availability and e ectiveness of the developed clustering
algorithm.