Intuitionistic fuzzy MST clustering algorithms

被引：59
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
收藏
页码：1130 / 1140
页数：11
相关论文
共 36 条
1234