| Title: | A self-supervised framework for clustering ensemble |
| Author: | Du, Liang (1)
; Shen, Yi-Dong (1)
; Shen, Zhiyong (4)
; Wang, Jianying (5)
; Xu, Zhiwu (1)
|
| Conference Name: | 14th International Conference on Web-Age Information Management, WAIM 2013
|
| Conference Date: | June 14, 2013 - June 16, 2013
|
| Issued Date: | 2013
|
| Conference Place: | Beidaihe, China
|
| Publish Place: | Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany
|
| Indexed Type: | EI
|
| ISSN: | 3029743
|
| ISBN: | 9783642385612
|
| Department: | (1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) Graduate University of Chinese Academy of Sciences, China; (3) University of Chinese Academy of Sciences, Beijing 100049, China; (4) Baidu Inc., Beijing 100085, China; (5) Computing Center, Shanghai University, Shanghai, China
|
| Abstract: | Clustering ensemble refers to combine a number of base clusterings for a particular data set into a consensus clustering solution. In this paper, we propose a novel self-supervised learning framework for clustering ensemble. Specifically, we treat the base clusterings as pseudo class labels and learn classifiers for each of them. By adding priors to the parameters of these classifiers, we capture the relationships between different base clusterings and meanwhile obtain a a single consolidated clustering result. In the proposed framework, we are able to incorporate the original data features to improve the performance of clustering ensemble. Another advantage, which distinguishes the proposed framework from the traditional clustering ensemble approaches, is with the generalization capability, i.e. it is able to assign the incoming data instances to the consensus clusters directly based on the original data features. We conduct extensive experiments on multiple real world data sets to show the effectiveness of our method. © 2013 Springer-Verlag Berlin Heidelberg. |
| English Abstract: | Clustering ensemble refers to combine a number of base clusterings for a particular data set into a consensus clustering solution. In this paper, we propose a novel self-supervised learning framework for clustering ensemble. Specifically, we treat the base clusterings as pseudo class labels and learn classifiers for each of them. By adding priors to the parameters of these classifiers, we capture the relationships between different base clusterings and meanwhile obtain a a single consolidated clustering result. In the proposed framework, we are able to incorporate the original data features to improve the performance of clustering ensemble. Another advantage, which distinguishes the proposed framework from the traditional clustering ensemble approaches, is with the generalization capability, i.e. it is able to assign the incoming data instances to the consensus clusters directly based on the original data features. We conduct extensive experiments on multiple real world data sets to show the effectiveness of our method. © 2013 Springer-Verlag Berlin Heidelberg. |
| Language: | 英语
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| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/16679
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| Appears in Collections: | 软件所图书馆_会议论文
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| Recommended Citation: | |
Du, Liang ,Shen, Yi-Dong ,Shen, Zhiyong ,et al. A self-supervised framework for clustering ensemble[C]. 见:14th International Conference on Web-Age Information Management, WAIM 2013. Beidaihe, China. June 14, 2013 - June 16, 2013.
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