Cluster validity index for adaptive clustering algorithms
Everyday a large number of records of surfing internet are generated. In various situations when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters’ pa...
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| Vydáno v: | IET communications Ročník 8; číslo 13; s. 2256 - 2263 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Stevenage
The Institution of Engineering and Technology
01.09.2014
John Wiley & Sons, Inc |
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| ISSN: | 1751-8628, 1751-8636 |
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| Abstract | Everyday a large number of records of surfing internet are generated. In various situations when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters’ partitions have different shapes, how to establish an algorithm to solve the clustering problem? Adaptive clustering algorithms can meet this challenge. Moreover, how to determinate the number of clusters when not only fuzzy cluster but also hard cluster are used? To address those problems, a new cluster validity index is proposed in this study. The proposed index focuses on the information of the geometrical structure of dataset by analysing the neighbourhood of data objects, which makes the index independent of the traditional fuzzy membership matrix. The new index consists of two parts, namely the ‘compactness’ and ‘separation measure’. The compactness indicates the degree of the similarity among the data objects in the same cluster. The separation measure indicates the degree of dissimilarity among the data objects in different clusters. The performance of their proposed index is excellent underpinned by the outcomes from the experiments based on both artificial datasets and real world datasets. |
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| AbstractList | Everyday a large number of records of surfing internet are generated. In various situations when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters’ partitions have different shapes, how to establish an algorithm to solve the clustering problem? Adaptive clustering algorithms can meet this challenge. Moreover, how to determinate the number of clusters when not only fuzzy cluster but also hard cluster are used? To address those problems, a new cluster validity index is proposed in this study. The proposed index focuses on the information of the geometrical structure of dataset by analysing the neighbourhood of data objects, which makes the index independent of the traditional fuzzy membership matrix. The new index consists of two parts, namely the ‘compactness’ and ‘separation measure’. The compactness indicates the degree of the similarity among the data objects in the same cluster. The separation measure indicates the degree of dissimilarity among the data objects in different clusters. The performance of their proposed index is excellent underpinned by the outcomes from the experiments based on both artificial datasets and real world datasets. Everyday a large number of records of surfing internet are generated. In various situations, when the authors are analysing internet data they do not know the cluster structure of the author's database of traffic features, such as when the border of cluster members is vague, and the clusters' partitions have different shapes, how to establish an algorithm to solve the clustering problem; adaptive clustering algorithms can meet this challenge. To address those problems, a new cluster validity index is proposed in this study. The proposed index focuses on the information of the geometrical structure of dataset by analysing the neighbourhood of data objects, which makes the index independent of the traditional fuzzy membership matrix. The new index consists of two parts, namely the 'compactness' and 'separation measure'. The compactness indicates the degree of the similarity among the data objects in the same cluster. The separation measure indicates the degree of dissimilarity among the data objects in different clusters. The performance of their proposed index is excellent underpinned by the outcomes from the experiments based on both artificial datasets and real world datasets. |
| Author | Liu, Yunjie Huang, Xu Xie, Mingzhi Cui, Hongyan Cai, Yunlong |
| Author_xml | – sequence: 1 givenname: Hongyan surname: Cui fullname: Cui, Hongyan email: Cuihy@bupt.edu.cn organization: 2Key Laboratory of Network System Architecture and Convergence, Beijing University of Post and Telecommunications, Beijing 100876, People's Republic of China – sequence: 2 givenname: Mingzhi surname: Xie fullname: Xie, Mingzhi organization: 2Key Laboratory of Network System Architecture and Convergence, Beijing University of Post and Telecommunications, Beijing 100876, People's Republic of China – sequence: 3 givenname: Yunlong surname: Cai fullname: Cai, Yunlong organization: 3Communication Lab of Huawei Technologies co. Ltd, Beijing 100085, People's Republic of China – sequence: 4 givenname: Xu surname: Huang fullname: Huang, Xu organization: 4Faculty of Education, Science, Technology & Maths, University of Canberra, Canberra 2601, Australia – sequence: 5 givenname: Yunjie surname: Liu fullname: Liu, Yunjie organization: 2Key Laboratory of Network System Architecture and Convergence, Beijing University of Post and Telecommunications, Beijing 100876, People's Republic of China |
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| Cites_doi | 10.1016/j.patcog.2009.09.018 10.1016/j.eswa.2012.05.049 10.1016/j.eswa.2010.06.032 10.1002/sam.10080 10.1109/ICDM.2010.35 10.1080/01969727408546059 10.1109/TFUZZ.2010.2087382 10.1080/01969727308546047 10.1109/TFUZZ.2010.2048114 10.1016/j.patcog.2004.04.007 10.1109/TKDE.2011.205 10.1016/j.ins.2009.11.005 10.1016/j.patrec.2009.07.009 10.1016/j.patrec.2010.08.007 10.1016/j.fss.2010.07.005 10.1016/j.patrec.2010.11.006 10.1007/BF02339490 10.1016/j.csda.2011.01.011 10.1007/978-1-4757-0450-1 10.1109/TSMCB.2010.2104319 10.1016/0165-0114(78)90016-7 10.1016/S0167-8655(97)00168-2 10.1016/j.patcog.2011.12.017 10.1049/iet-cvi.2012.0187 10.1016/S0031-3203(98)00157-5 10.1109/TFUZZ.2011.2106216 10.1109/34.85677 |
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| Keywords | artificial datasets traffic features data analysis data objects hard cluster index separation measure index compactness author database cluster members adaptive clustering algorithms Internet data analysis pattern clustering cluster partitions cluster structure cluster validity index Internet real world datasets dataset geometrical structure fuzzy membership matrix |
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| SubjectTerms | Adaptive algorithms adaptive clustering algorithms Algorithms artificial datasets author database cluster members cluster partitions cluster structure cluster validity index Clustering Clusters data analysis data objects dataset geometrical structure fuzzy membership matrix hard cluster index compactness index separation measure Internet Internet data analysis pattern clustering Performance indices real world datasets Separation traffic features |
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