Intuitionistic fuzzy MST clustering algorithms

被引：60

作者：

Zhao, Hua ^{[1
,2
]}

Xu, Zeshui ^{[1
]}

Liu, Shousheng ^{[1
]}

Wang, Zhong ^{[1
]}

机构：

[1] PLA Univ Sci & Technol, Inst Sci, Nanjing 210007, Jiangsu, Peoples R China

[2] PLA Univ Sci & Technol, Inst Commun Engn, Nanjing 210007, Jiangsu, Peoples R China

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2012年 / 62卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Intuitionistic fuzzy set; Minimum spanning tree; Interval-valued intuitionistic fuzzy set; Graph theory-based clustering algorithm; Intuitionistic fuzzy distance; VAGUE SET-THEORY; SIMILARITY MEASURES; DECISION-MAKING; AGGREGATION OPERATORS; NETWORKS; DISTANCE;

D O I：

10.1016/j.cie.2012.01.007

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we investigate graph theory-based clustering techniques for Atanassov's intuitionistic fuzzy sets (A-IFSs) and interval-valued intuitionistic fuzzy sets (IVIFSs). We start by introducing the concepts of graph, minimum spanning tree (MST), A-IFS, and intuitionistic fuzzy distance, and develop two intuitionistic fuzzy MST clustering algorithms (Algorithms I and II). Then we extend Algorithm II for clustering IVIFSs, and show the effectiveness of our algorithms through some numerical experiments. (C) 2012 Published by Elsevier Ltd.

引用

页码：1130 / 1140

页数：11

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