scholarly journals APPLICATION OF GENERALIZED DISTANCE MEASURE TO THE CONSTRUCTION OF A SYNTHETIC INDEX OF SUBJECTIVE SENSE OF FINANCIAL SECURITY OF FARMERS’ HOUSEHOLDS

2017 ◽  
Vol 18 (3) ◽  
pp. 501-509 ◽  
Author(s):  
Andrzej Wołoszyn ◽  
Romana Głowicka-Wołoszyn ◽  
Agnieszka Kozera
Keyword(s):  
Distance Measure ◽  
Financial Security ◽  
Synthetic Index ◽  
Generalized Distance

scholarly journals An Extended VIKOR Method for Multiple Attribute Decision Analysis with Bidimensional Dual Hesitant Fuzzy Information

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Min Xue ◽  
Xiaoan Tang ◽  
Nanping Feng
Keyword(s):  
Decision Analysis ◽  
Fuzzy Set ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Set ◽  
Distance Measures ◽  
Hesitant Fuzzy Set ◽  
Entropy Measure ◽  
Vikor Method ◽  
Multiple Attribute ◽  
Generalized Distance

Bidimensional dual hesitant fuzzy (BDHF) set is developed to present preferences of a decision maker or an expert, which is more objective than existing fuzzy sets such as Atanassov’s intuitionistic fuzzy set, hesitant fuzzy set, and dual hesitant fuzzy set. Then, after investigating some distance measures, we define a new generalized distance measure between two BDHF elements with parameterλfor the sake of overcoming some drawbacks in existing distance measures. Covering all possible values of parameterλ, a new approach is designed to calculate the generalized distance measure between two BDHF elements. In order to address complex multiple attribute decision analysis (MADA) problems, an extension of fuzzy VIKOR method in BDHF context is proposed in this paper. In VIKOR method for MADA problems, weight of each attribute indicates its relative importance. To obtain weights of attributes objectively, a new entropy measure with BDHF information is developed to create weight of each attribute. Finally, an evaluation problem of performance of people’s livelihood project in several regions is analyzed by the proposed VIKOR method to demonstrate its applicability and validity.

scholarly journals Single-Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method

2014 ◽  
Vol 23 (3) ◽  
pp. 311-324 ◽  
Author(s):  
Jun Ye
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Spanning Tree ◽  
Clustering Algorithm ◽  
Minimum Spanning Tree ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Sets ◽  
Clustering Method ◽  
Neutrosophic Sets ◽  
Generalized Distance

AbstractClustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a 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 article proposes a single-valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an 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.

A Generalized Distance Measure for Integrating Multiple Environmental Assessment Indicators

2006 ◽  
Vol 21 (4) ◽  
pp. 469-476 ◽  
Author(s):  
Liem T. Tran ◽  
Robert V. O’Neill ◽  
Elizabeth R. Smith
Keyword(s):  
Environmental Assessment ◽  
Distance Measure ◽  
Generalized Distance

Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

2014 ◽  
Vol 23 (4) ◽  
pp. 379-389 ◽  
Author(s):  
Jun Ye
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Clustering Methods ◽  
Neutrosophic Sets ◽  
Generalized Distance

AbstractClustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.

scholarly journals Multidimensional Assessment of Polish Farm Household Financial Security by TOPSIS and Generalised Distance Measure

2021 ◽  
Vol XXIV (Issue 4) ◽  
pp. 534-553
Author(s):  
Andrzej Woloszyn ◽  
Joanna Stanislawska ◽  
Romana Głowicka-Woloszyn ◽  
Agnieszka Kozera ◽  
Anna Rosa
Keyword(s):  
Distance Measure ◽  
Farm Household ◽  
Financial Security ◽  
Multidimensional Assessment

scholarly journals Similarity Measures between Intuitionistic Fuzzy Credibility Sets and Their Multicriteria Decision-Making Method for the Performance Evaluation of Industrial Robots

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jun Ye ◽  
Shigui Du ◽  
Rui Yong
Keyword(s):  
Decision Making ◽  
Performance Evaluation ◽  
Distance Measure ◽  
Similarity Measures ◽  
Multicriteria Decision Making ◽  
Industrial Robots ◽  
Multicriteria Decision ◽  
Intuitionistic Fuzzy ◽  
Generalized Distance ◽  
Credibility Degree

To enhance the credibility level/measure of an intuitionistic fuzzy set (IFS), this study proposes the notion of an intuitionistic fuzzy credibility set (IFCS) to express the hybrid information of a pair of a membership degree and a credibility degree and a pair of a nonmembership degree and a credibility degree. Next, we propose generalized distance and similarity measures between IFCSs and then further generalize the weighted generalized distance measure of IFCSs to the trigonometric function-based similarity measures of IFCSs, including the cosine, sine, tangent, and cotangent similarity measures based on the weighted generalized distance measure of IFCSs. Then, a multicriteria decision making (MCDM) method using the proposed similarity measures is developed in the environment of IFCSs. An illustrative example about the performance evaluation of industrial robots and comparative analysis are presented to indicate the applicability and efficiency of the developed method in the setting of IFCSs.

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