Knowledge Commons of Institute of Automation,CAS
Efficient Clustering Aggregation Based on Data Fragments | |
Wu, Ou1; Hu, Weiming1; Maybank, Stephen J.2; Zhu, Mingliang1; Li, Bing1; Ou Wu | |
发表期刊 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS |
2012-06-01 | |
卷号 | 42期号:3页码:913-926 |
文章类型 | Article |
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. |
关键词 | Clustering Aggregation Comparison Measure Computational Complexity Data Fragment Fragment-based Approach Mutual Information Point-based Approach |
WOS标题词 | Science & Technology ; Technology |
关键词[WOS] | ENSEMBLE ; CLASSIFIERS ; PARTITIONS ; ALGORITHM ; CONSENSUS ; NET |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Automation & Control Systems ; Computer Science |
WOS类目 | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
WOS记录号 | WOS:000304163200027 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/3276 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Efficient Clustering(798KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论