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| cluster ensembles via weighted graph regularized nonnegative matrix factorization | |
| Du Liang; Li Xuan; Shen Yi-Dong | |
| 2011 | |
| Conference Name | 7th International Conference on Advanced Data Mining and Applications, ADMA 2011 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 215-228 |
| Conference Date | December 1 |
| Conference Place | Beijing, China |
| Indexed Type | EI |
| ISSN | 0302-9743 |
| ISBN | 9783642258527 |
| Department | (1) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) Graduate University Chinese Academy of Sciences Beijing 100049 China |
| English Abstract | Cluster ensembles aim to generate a stable and robust consensus clustering by combining multiple different clustering results of a dataset. Multiple clusterings can be represented either by multiple co-association pairwise relations or cluster based features. Traditional clustering ensemble algorithms learn the consensus clustering using either of the two representations, but not both. In this paper, we propose to integrate the two representations in a unified framework by means of weighted graph regularized nonnegative matrix factorization. Such integration makes the two representations complementary to each other and thus outperforms both of them in clustering accuracy and stability. Extensive experimental results on a number of datasets further demonstrate this. © 2011 Springer-Verlag.; Cluster ensembles aim to generate a stable and robust consensus clustering by combining multiple different clustering results of a dataset. Multiple clusterings can be represented either by multiple co-association pairwise relations or cluster based features. Traditional clustering ensemble algorithms learn the consensus clustering using either of the two representations, but not both. In this paper, we propose to integrate the two representations in a unified framework by means of weighted graph regularized nonnegative matrix factorization. Such integration makes the two representations complementary to each other and thus outperforms both of them in clustering accuracy and stability. Extensive experimental results on a number of datasets further demonstrate this. © 2011 Springer-Verlag. |
| Keyword | Clustering Algorithms Data Mining Factorization Graphic Methods |
| Sponsorship | IBM Research; China Samsung Telecom R and D Center; Tsinghua University |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16261 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Du Liang,Li Xuan,Shen Yi-Dong. cluster ensembles via weighted graph regularized nonnegative matrix factorization[C],2011:215-228. |
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