A self-supervised framework for clustering ensemble
文献类型：会议论文
| 作者 | Du, Liang (1) ; Shen, Yi-Dong (1) ; Shen, Zhiyong (4) ; Wang, Jianying (5) ; Xu, Zhiwu (1) |
| 出版日期 | 2013 |
| 会议名称 | 14th International Conference on Web-Age Information Management, WAIM 2013 |
| 会议日期 | June 14, 2013 - June 16, 2013 |
| 会议地点 | Beidaihe, China |
| 页码 | 253-264 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议录出版地 | Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany |
| 语种 | 英语 |
| ISSN号 | 3029743 |
| ISBN号 | 9783642385612 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16679]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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