中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
Title:
cluster ensembles via weighted graph regularized nonnegative matrix factorization
Author: Du Liang ; Li Xuan ; Shen Yi-Dong
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name: 7th International Conference on Advanced Data Mining and Applications, ADMA 2011
Conference Date: December 1
Issued Date: 2011
Conference Place: Beijing, China
Keyword: Clustering algorithms ; Data mining ; Factorization ; Graphic methods
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
Sponsorship: IBM Research; China Samsung Telecom R and D Center; Tsinghua University
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16261
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
Du Liang,Li Xuan,Shen Yi-Dong. cluster ensembles via weighted graph regularized nonnegative matrix factorization[C]. 见:7th International Conference on Advanced Data Mining and Applications, ADMA 2011. Beijing, China. December 1.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Du Liang]'s Articles
[Li Xuan]'s Articles
[Shen Yi-Dong]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Du Liang]‘s Articles
[Li Xuan]‘s Articles
[Shen Yi-Dong]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

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

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace