Efficient Clustering Aggregation Based on Data Fragments
文献类型：期刊论文
| 作者 | Wu, Ou1 ; Hu, Weiming1; Maybank, Stephen J.2; Zhu, Mingliang1; Li, Bing1; Ou Wu
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
 |
| 出版日期 | 2012-06-01 |
| 卷号 | 42期号:3页码:913-926 |
| 关键词 | Clustering aggregation comparison measure computational complexity data fragment fragment-based approach mutual information point-based approach |
| 英文摘要 | Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| 研究领域[WOS] | Automation & Control Systems ; Computer Science |
| 关键词[WOS] | ENSEMBLE ; CLASSIFIERS ; PARTITIONS ; ALGORITHM ; CONSENSUS ; NET |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000304163200027 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3276]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 通讯作者 | Ou Wu |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ London, Birkbeck Coll, Sch Comp Sci & Informat Syst, London WC1E 7HX, England |
| 推荐引用方式 GB/T 7714 | Wu, Ou,Hu, Weiming,Maybank, Stephen J.,et al. Efficient Clustering Aggregation Based on Data Fragments[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2012,42(3):913-926. |
| APA | Wu, Ou,Hu, Weiming,Maybank, Stephen J.,Zhu, Mingliang,Li, Bing,&Ou Wu.(2012).Efficient Clustering Aggregation Based on Data Fragments.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,42(3),913-926. |
| MLA | Wu, Ou,et al."Efficient Clustering Aggregation Based on Data Fragments".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 42.3(2012):913-926. |
入库方式： OAI收割
来源：自动化研究所
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