Neutrosophic Logic-based DIANA Clustering algorithm

Abstract

On the one hand, the most extensively used Hierarchical Clustering techniques are the Hierarchical
Divisive Clustering (HDC) algorithms such as DIANA. Its primary goal is to build the tree of Hierarchical
Agglomerative Clustering (HAC) in reverse order. On the other hand, Neutrosophy is an extension of fuzzy
logic and serves as a model of uncertainty. In addition to the truth (T) and falsity (F) elements of fuzzy
logic, single-valued Neutrosophic sets (SVNs) logic estimates the proportion of indeterminacy (I) for a given
proposition. In this work, we propose a Neutrosophic Logic-based DIANA Clustering algorithm. Indeterminacy
is added to the DIANA hierarchical clustering algorithm using single-valued Neutrosophic sets (SVNs). The
suggested algorithm is named Neutro-DIANA (Neutrosophic DIANA) and is broken down into numerous steps.
The experimental findings show that the suggested technique for dealing with indeterminacy is effective.