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

2020 ◽  
Vol 5 (2) ◽  
pp. 121-127
Author(s):  
Tamilarasu Sangeetha ◽  
Geetha Mary Amalanathan
Keyword(s):  
Outlier Detection ◽  
Neutrosophic Sets ◽  
Weighted Density ◽  
Density Method

An Intuitionistic Fuzzy Approach With Rough Entropy Measure to Detect Outliers in Two Universal Sets

2020 ◽  
Vol 9 (3) ◽  
pp. 100-117
Author(s):  
Sangeetha T. ◽  
Geetha Mary A.
Keyword(s):  
Outlier Detection ◽  
Categorical Data ◽  
Numerical Data ◽  
Entropy Measure ◽  
Weighted Density ◽  
Intuitionistic Fuzzy ◽  
Decision Attributes ◽  
Density Values ◽  
Class Labels ◽  
Massive Databases

The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.

Classic Outlier Detection from Web Clusters using Disimilarity Measure

2012 ◽  
Vol 2 (3) ◽  
pp. 98-101 ◽  
Author(s):  
E.Sateesh E.Sateesh ◽  
◽  
M.L.Prasanthi M.L.Prasanthi
Keyword(s):  
Outlier Detection ◽  
Web Clusters
Sign in / Sign up

Export Citation Format

Share Document