Comparing non-parametric ensemble methods for document clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5039 LNCS, Issue , 2008, Pages 245-256

  Gonzàlez, Edgar ^a   Turmo, Jordi ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING METHODS; DOCUMENT CLUSTERING; DOCUMENT COLLECTIONS; ENSEMB LE METHODS; HEIDELBERG (CO); INDIVIDUAL (PSS 544-7); INTERNATIONAL CONFERENCES; NATURAL LANGUAGES; NON-PARAMETRIC; OPTIMAL SOLUTIONS;

FLOW OF SOLIDS; INFORMATION RETRIEVAL; INFORMATION SCIENCE; INFORMATION SYSTEMS; LEAD; LINGUISTICS;

CLUSTERING ALGORITHMS;

EID: 47749151509     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69858-6_25     Document Type: Conference Paper

References (19)

1. Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proc. of CIKM (2002)
2. Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3) (2004)
3. Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrica 50 (1985)
4. Li, T., Ma, S., Ogihara, M.: Document clustering via adaptive subspace iteration. In: Proc. of SIGIR (2004)
5. Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society Series B 63(2) (2001)
6. Fraley, C., Raftery, A.: How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal 41(8) (1998)
7. Surdeanu, M., Turmo, J., Ageno, A.: A hybrid unsupervised approach for document clustering. In: Proc. of KDD (2005)
8. Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: Models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12) (2005)
9. Strehl, A., Ghosh, J.: Cluster ensembles - A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3 (2002)
10. Siersdorfer, S., Sizov, S.: Restrictive clustering and metaclustering for selforganizing document collections. In: Proc. of SIGIR (2004)
11. Greene, D., Cunningham, P.: Efficient ensemble methods for document clustering. Technical report, Department of Computer Science, Trinity College Dublin (2006)
12. Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. In: Proc. of ICDE (2005)
13. Fred, A., Jain, A.: Robust data clustering. In: Proc. of CVPR (2003)
14. Li, T., Ogihara, M., Ma, S.: On combining multiple clusterings. In: Proc. of CIKM (2004)
15. Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3) (2000)
16. Slonim, N.: The Information Bottleneck: Theory and Applications. PhD thesis, The Hebrew University (2003)
17. Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Communications in Statistics 3 (1974)
18. Dhillon, I., Guan, Y.: Information theoretic clustering of sparse co-occurrence data. In: Proc. of ICDM (2003)
19. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006)