Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Abstract

Neutrosophy is the study of neutralities, which is an extension of discussing the truth of opinions. Neutrosophiclogic can be applied to any field, to provide the solution for indeterminacy problem. Many of the real-world datahave a problem of inconsistency, indeterminacy and incompleteness. Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information, but both concepts failed to handle indeterminateinformation. To handle this complicated situation, researchers require a powerful mathematical tool, naming,neutrosophic sets, which is a generalised concept of fuzzy and intuitionistic fuzzy sets. Neutrosophic sets providea solution for both incomplete and indeterminate information. It has mainly three degrees of membership suchas truth, indeterminacy and falsity. Boolean values are obtained from the three degrees of membership by cut relationmethod. Data items which contrast from other objects by their qualities are outliers. The weighted density outlierdetection method based on rough entropy calculates weights of each object and attribute. From the obtained weightedvalues, the threshold value is fixed to determine outliers. Experimental analysis of the proposed method hasbeen carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to proveits performance.