CASIA OpenIR  > 模式识别国家重点实验室  > 视频内容安全
Efficient Clustering Aggregation Based on Data Fragments
Wu, Ou1; Hu, Weiming1; Maybank, Stephen J.2; Zhu, Mingliang1; Li, Bing1; Ou Wu
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
2012-06-01
Volume42Issue:3Pages:913-926
SubtypeArticle
AbstractClustering 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.
KeywordClustering Aggregation Comparison Measure Computational Complexity Data Fragment Fragment-based Approach Mutual Information Point-based Approach
WOS HeadingsScience & Technology ; Technology
WOS KeywordENSEMBLE ; CLASSIFIERS ; PARTITIONS ; ALGORITHM ; CONSENSUS ; NET
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000304163200027
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3276
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorOu Wu
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Efficient Clustering(798KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wu, Ou]'s Articles
[Hu, Weiming]'s Articles
[Maybank, Stephen J.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wu, Ou]'s Articles
[Hu, Weiming]'s Articles
[Maybank, Stephen J.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wu, Ou]'s Articles
[Hu, Weiming]'s Articles
[Maybank, Stephen J.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Efficient Clustering Aggregation based on DataFragments.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.