Restrictive clustering and metaclustering for self-organizing document collections

Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2004, Pages 226-233

  Siersdorfer, Stefan ^a   Sizov, Sergej ^a  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

Meta Clustering; Restrictive Clustering

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; METADATA; SEARCH ENGINES; VECTORS; WORLD WIDE WEB;

DOCUMENT COLLECTION; META CLUSTERING; REAL-WORLD DATA COLLECTION; RESTRICTIVE CLUSTERING;

INFORMATION RETRIEVAL;

EID: 8644269720     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1008992.1009032     Document Type: Conference Paper

References (21)

1. The 20 newsgroups data set. http://www.ai.mit.edu/~jrennie/20Newsgroups/.
2. Internet movie database, http://www.imdb.com.
3. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB. 1994.
4. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.
5. M. W. Berry, S. T. Dumais, and G. W. O'Brien. Using linear algebra for intelligent information retrieval. Technical Report UT-CS-94-270, 1994.
6. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
7. C. Burges. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 1998.
8. M. Ester, H.-P. Kriegel, and J. Sander. Knowledge Discovery in Databases. Springer, 2001.
9. A. Fred and A. K. Jain. Robust data clustering. In Proc. Conference on Computer Vision and Pattern Recognition, CVPR, 2003.
10. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
11. J. Hartigan and M. Wong. A k-Means clustering algorithm. Applied Statistics, 28:100-108, 1979.
12. M. Hasan and Y. Masumoto. Document clustering: before and after the singular value decomposition. Technical Report Information Processing Society of Japan, Natural Language, No.134, 1999.
13. D. D. Lewis. Evaluating text categorization. In Proceedings of Speech and Natural Language Workshop, pages 312-318. Defense Advanced Research Projects Agency, Morgan Kaufmann, Feb. 1991
14. W. Madison, Y. Yang, and J. Pedersen. A comparative study on feature selection in text categorization. In ICML, 1997.
15. C. Manning and H. Schuetze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
16. C. V. Rijsbergen. A theoretical basis for the use of co-occurence data in information retrieval. Journal of Documentation, 33:2, pp. 106-119, 1977.
17. S. Siersdorfer and S. Sizov. Construction of feature spaces and meta methods for classification of Web documents. Conference on Database Systems for Business, Technology and Web (BTW), 2003.
18. S. Sizov, M. Biwer, J. Graupmann, S. Siersdorfer, M. Theobald, G. Weikum, and P. Zimmer. The BINGO! system for information portal generation and expert Web search. Conference on Innovative Systems Research (CIDR), 2003.
19. S. Sizov, S. Siersdorfer, M. Theobald, and G. Weikum. BINGO!: Bookmark-induced gathering of information. International Conference on Web Information Systems Engineering (WISE), 2002.
20. A. Strehl and J. Gosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, pp. 583-617, 2002.
21. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.