Scalable community discovery on textual data with relations

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 1203-1212

  Li, Huajing ^a   Nie, Zaiqing ^b   Lee, Wang Chien ^a   Giles, C Lee ^a,c   Wen, Ji Rong ^b  

^a Department of Computer Science and Engineering ^*  (United States)

^b MICROSOFT RESEARCH ASIA   (China)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Clustering; Community mining; Relational data

Indexed keywords

CLUSTERING; CLUSTERING TECHNIQUES; COMMUNITY MINING; COMMUNITY MODEL; CORPUS SIZE; DATA CLUSTERING; INITIAL PARAMETER; LARGE DOCUMENT CORPORA; LARGE-SCALE DATASETS; PRECISION IMPROVEMENT; RELATION ANALYSIS; RELATIONAL DATA; TEXTUAL DATA;

CLUSTERING ALGORITHMS; KNOWLEDGE MANAGEMENT; SCALABILITY;

CLUSTER ANALYSIS;

EID: 70349260835     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458082.1458241     Document Type: Conference Paper

References (23)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington, D.C., 26-28 1993.
2. E. Airoldi, D. Blei, E. Xing, and S. Fienberg. A latent mixed membership model for relational data. In LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery, pages 82-89, New York, NY, USA, 2005. ACM Press.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
4. Y. Chi, S. Zhu, X. Song, J. Tatemura, and B. L. Tseng. Structural and temporal analysis of the blogosphere through community factorization. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 163-172, NewYork, NY, USA, 2007. ACM Press.
5. D. Cohn and H. Chang. Learning to probabilistically identify authoritative documents. In Proc. 17th International Conf. on Machine Learning, pages 167-174. Morgan Kaufmann, San Francisco, CA, 2000.
6. D. A. Cohn and T. Hofmann. The missing link - a probabilistic model of document content and hypertext connectivity. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS, pages 430-436. MIT Press, 2000.
7. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic nalysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
8. Y. Dourisboure, F. Geraci, and M. Pellegrini. Extraction and classification of dense communities in the web. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 461-470, New York, NY, USA, 2007. ACM Press.
9. G. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 150-160, Boston, MA, August 20-23 2000.
10. B. Gao, T.-Y. Liu, X. Zheng, Q.-S. Cheng, and W.-Y. Ma. Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering. In KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 41-50, New York, NY, USA, 2005. ACM Press.
11. D. Gibson, J. M. Kleinberg, and P. Raghavan. Inferring web communities from link topology. In UK Conference on Hypertext, pages 225-234, 1998.
12. T. L. Griffiths and M. Steyvers. Finding scientific topics. Proc Natl Acad Sci U S A, 101 Suppl 1:5228-5235, April 2004.
13. D. Harel and Y. Koren. Clustering spatial data using random walks. In Knowledge Discovery and Data Mining (KDD'01), pages 281-286, 2001.
14. T. Hofmann. Probabilistic latent semantic analysis. In Proc. of Uncertainty in Artificial Intelligence, UAI'99, Stockholm, 1999.
15. M. Kitsuregawa, M. Toyoda, and I. Pramudiono. Web community mining and web log mining: commodity cluster based execution. Aust. Comput. Sci. Commun., 24(2):3-10, 2002.
16. J. M. Kleinberg. Hubs, authorities, and communities. ACM Comput. Surv., page 5.
17. B. Long, Z. M. Zhang, and P. S. Yu. A probabilistic framework for relational clustering. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 470-479, New York, NY, USA, 2007. ACM.
18. D. Mimno and A. McCallum. Expertise modeling for matching papers with reviewers. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 500-509, New York, NY, USA, 2007. ACM.
19. F. Moser, R. Ge, and M. Ester. Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 510-519, New York, NY, USA, 2007. ACM.
20. A. Popescul, G. Flake, S. Lawrence, L. Ungar, and C. L. Giles. Clustering and identifying temporal trends in document databases. In Advances in Digital Libraries, ADL 2000, pages 173-182, Washington, DC, 2000.
21. M. Rosen-Zvi, T. Griffiths, P. Smyth, and M. Steyvers. Learning author topic models from text corpora. Technical report, November 2005.
22. X. Wang, N. Mohanty, and A. McCallum. Group and topic discovery from relations and text. In LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery, pages 28-35, New York, NY, USA, 2005. ACM Press.
23. W.-J. Zhou, J.-R. Wen, W.-Y. Ma, and H.-J. Zhang. A concentric-circle model for community mining in graph structures. Technical Report MSR-TR-2002-123, Microsoft Research Asia, Beijing, China, November 2002.