Comparison of Different Fuzzy Clustering Algorithms: A Replicated Case Study

被引：3
|
作者：
Singh, Tusharika [1 ]
Gosain, Anjana [1 ]
机构：
[1] Guru Gobind Singh Indraprastha Univ, Univ Sch Informat & Commun Technol, Delhi 110078, India
来源：
INFORMATION AND DECISION SCIENCES | 2018年 / 701卷
关键词：
Fuzzy clustering; FCM; PCM; PFCM; IFCM; KFCM; DOFCM; Outliers;
D O I：
10.1007/978-981-10-7563-6_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Fuzzy clustering partitions data points of a dataset into clusters in which one data point can belong to more than one cluster. In the literature, a number of fuzzy clustering algorithms have been proposed. This paper reviews various fuzzy clustering algorithms such as Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), Possibilistic Fuzzy C-Means (PFCM), Intuitionistic Fuzzy C-Means (IFCM), Kernel Fuzzy C-Means (KFCM), and Density-Oriented Fuzzy C-Means (DOFCM). We have demonstrated the experimental performance of these algorithms on some standard and synthetic datasets which include-Bensaid, Square (DUNN), D15, and D45 dataset. Then, the results are analyzed and compared to see the effectiveness of these algorithms in presence of noise and outliers.
引用
收藏
页码：267 / 275
页数：9
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