Fuzziness parameter selection in fuzzy c-means： The perspective of cluster validation

Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clusterin...

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Vydáno v:Science China. Information sciences Ročník 57; číslo 11; s. 247 - 254
Hlavní autoři: Zhou, KaiLe, Fu, Chao, Yang, ShanLin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Science China Press 01.11.2014
Springer Nature B.V
Témata:
Algorithms
Clustering
Clusters
Computer Science
Datasets
FCM算法
Fuzzy
Fuzzy logic
Fuzzy set theory
Information Systems and Communication Service
Optimization
Parameters
Pattern recognition
Research Paper
Synthetic data
参数选择
有效性指数
模糊c均值
群集
聚类方法
聚类结果
验证
fuzziness parameter
cluster validity index
clustering
cluster validation
fuzzy c-means (FCM)
ISSN:1674-733X, 1869-1919
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Popis
Shrnutí:Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].
Bibliografie:11-5847/TP
clustering, fuzziness parameter, fuzzy c-means （FCM）, cluster validation, cluster validity index
Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].
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ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-014-5146-0