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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| Vydané v: | Science China. Information sciences Ročník 57; číslo 11; s. 247 - 254 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Science China Press
01.11.2014
Springer Nature B.V |
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| ISSN: | 1674-733X, 1869-1919 |
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| Abstract | 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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| AbstractList | 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]. 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]. 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]. |
| Author | ZHOU KaiLe FU Chao YANG ShanLin |
| AuthorAffiliation | School of Management, Hefei University of Technology, Hefei 230009, China Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education Hefei 230009, China |
| Author_xml | – sequence: 1 givenname: KaiLe surname: Zhou fullname: Zhou, KaiLe email: kailezhou@gmail.com organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education – sequence: 2 givenname: Chao surname: Fu fullname: Fu, Chao organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education – sequence: 3 givenname: ShanLin surname: Yang fullname: Yang, ShanLin organization: School of Management, Hefei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education |
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| Keywords | fuzziness parameter cluster validity index clustering cluster validation fuzzy c-means (FCM) |
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| Notes | 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]. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| SubjectTerms | 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 |
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