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RankClus: integrating clustering with ranking for heterogeneous information network analysis

RankClus: integrating clustering with ranking for heterogeneous information network analysis RankClus: Integrating Clustering with Ranking for Heterogeneous Information Network Analysis — Yizhou Sun , Jiawei Han , Peixiang Zhao , Zhijun Yin , Hong Cheng ¡ , Tianyi Wu ¡ Department of Computer Science Dept. of Syst. Eng. & Eng. Mgmnt University of Illinois at Urbana Champaign The Chinese University of Hong Kong {sun22,hanj,pzhao4,zyin3,twu5}@uiuc.edu hcheng@se.cuhk.edu.hk ABSTRACT As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects (e.g., thousands of authors) in one huge cluster without distinction is dull as well. In this paper, we address the problem of generating clusters for a speci ed type of objects, as well as ranking information for all types of objects based on these clusters in a multityped (i.e., heterogeneous) information network. A novel clustering framework called RankClus is proposed that directly generates clusters integrated with ranking. Based on initial http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

RankClus: integrating clustering with ranking for heterogeneous information network analysis

Association for Computing Machinery — Mar 24, 2009

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References (19)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2009 by ACM Inc.
ISBN
978-1-60558-422-5
doi
10.1145/1516360.1516426
Publisher site
See Article on Publisher Site

Abstract

RankClus: Integrating Clustering with Ranking for Heterogeneous Information Network Analysis — Yizhou Sun , Jiawei Han , Peixiang Zhao , Zhijun Yin , Hong Cheng ¡ , Tianyi Wu ¡ Department of Computer Science Dept. of Syst. Eng. & Eng. Mgmnt University of Illinois at Urbana Champaign The Chinese University of Hong Kong {sun22,hanj,pzhao4,zyin3,twu5}@uiuc.edu hcheng@se.cuhk.edu.hk ABSTRACT As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects (e.g., thousands of authors) in one huge cluster without distinction is dull as well. In this paper, we address the problem of generating clusters for a speci ed type of objects, as well as ranking information for all types of objects based on these clusters in a multityped (i.e., heterogeneous) information network. A novel clustering framework called RankClus is proposed that directly generates clusters integrated with ranking. Based on initial

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