Intuitionistic fuzzy C-means clustering algorithms

被引：134
|
作者：
Xu, Zeshui [1 ,2 ]
Wu, Junjie [3 ]
机构：
[1] Southeast Univ, Sch Econ & Management, Nanjing 210096, Peoples R China
[2] PLA Univ Sci & Technol, Inst Sci, Nanjing 210007, Peoples R China
[3] Beihang Univ, Dept Informat Syst, Sch Econ & Management, Beijing 100191, Peoples R China
来源：
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2010年 / 21卷 / 04期
基金：
中国国家自然科学基金;
关键词：
intuitionistic fuzzy set (IFS); intuitionistic fuzzy C-means algorithm; clustering; interval-valued intuitionistic fuzzy set (IVIFS); SIMILARITY MEASURES; SETS;
D O I：
10.3969/j.issn.1004-4132.2010.04.009
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Intuitionistic fuzzy sets (IFSs) are useful means to describe and deal with vague and uncertain data. An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed. In each stage of the intuitionistic fuzzy C-means method the seeds are modified, and for each IFS a membership degree to each of the clusters is estimated. In the end of the algorithm, all the given IFSs are clustered according to the estimated membership degrees. Furthermore, the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets (IVIFSs). Finally, the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.
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页码：580 / 590
页数：11
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