Statistical semantics for enhancing document clustering

Knowledge and Information Systems

Volumn 28, Issue 2, 2011, Pages 365-393

  Farahat, Ahmed K ^a   Kamel, Mohamed S ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Document clustering; Semantic similarity; Statistical semantics; Term term correlations

Indexed keywords

CLUSTER ANALYSIS; INFORMATION RETRIEVAL; NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS; VECTOR SPACES;

DOCUMENT CLUSTERING; DOCUMENT REPRESENTATION; REPRESENTATION MODEL; SEMANTIC SIMILARITY; SPECIFIC SEMANTICS; STATISTICAL SEMANTICS; TERM-TERM CORRELATIONS; VECTOR SPACE MODELS;

CLUSTERING ALGORITHMS;

EID: 79961211393     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0367-z     Document Type: Article

References (41)

1. Cai D, He X, Han J (2005) Document clustering using locality preserving indexing. IEEE Trans Knowl Data Eng 17(12): 1624-1637.
2. Carbonell J, Yang Y, Frederking R, Brown R, Geng Y, Lee D (1997) Translingual information retrieval: A comparative evaluation. In: Proceedings of the fifteenth international joint conference on artificial intelligence. Morgan Kaufmann, San Mateo, pp 708-715.
3. Cristianini N, Shawe-Taylor J, Lodhi H (2002) Latent semantic kernels. J Intell Inf Syst 18(2): 127-152.
4. Deerwester S, Dumais S, Furnas G, Landauer T, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci Technol 41(6): 391-407.
5. Dhillon I (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 269-274.
6. Dhillon I, Kogan J, Nicholas C (2003) Feature selection and document clustering. In: Berry M (eds) Survey of Text Mining. Springer, New York, pp 73-100.
7. Dhillon IS, Modha DS (2001) Concept decompositions for large sparse text data using clustering. Mach Learn 42(1/2): 143-175.
8. Ding C, Li T, Jordan MI (2010) Convex and semi-nonnegative matrix factorizations. IEEE Trans Pattern Anal Mach Intell 32: 45-55.
9. Dongen S (2000) Performance criteria for graph clustering and Markov cluster experiments. Technical report, CWI (Centre for Mathematics and Computer Science), Amsterdam, The Netherlands.
10. Drineas P, Kannan R, Mahoney M (2007) Fast Monte Carlo algorithms for matrices I: approximating matrix multiplication. SIAM J Comput 36(1): 132-157.
11. Farahat AK, Kamel MS (2009) Document clustering using semantic kernels based on term-term correlations. In: Proceedings of the 2009 IEEE international conference on data mining workshops. IEEE Computer Society, Washington, DC, pp 459-464.
12. Farahat AK, Kamel MS (2010) Enhancing document clustering using hybrid models for semantic similarity. In: Proceedings of the eighth workshop on text mining at the tenth SIAM international conference on data mining. SIAM, Philadelphia, pp 83-92.
13. Fung B, Wang K, Ester M (2003) Hierarchical document clustering using frequent itemsets. In: Proceedings of the third SIAM international conference on data mining. SIAM, Philadelphia, pp 59-70.
14. Furnas G, Landauer T, Gomez L, Dumais S (1983) Statistical semantics: analysis of the potential performance of keyword information systems. Bell Syst Tech J 62(6): 1753-1806.
15. Gabrilovich E, Markovitch S (2007) Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: Proceedings of the twentieth international joint conference on artificial intelligence. Morgan Kaufmann, San Mateo, pp 6-12.
16. Han E, Boley D, Gini M, Gross R, Hastings K, Karypis G, Kumar V, Mobasher B, Moore J (1998) WebACE: a web agent for document categorization and exploration. In: Proceedings of the second international conference on autonomous agents. ACM, New York, pp 408-415.
17. He X, Zha H, Ding C, Simon H (2002) Web document clustering using hyperlink structures. Comput Stat Data Anal 41(1): 19-45.
18. Hotho A, Staab S, Stumme G (2003) WordNet improves text document clustering. In: Proceedings of the SIGIR 2003 semantic web workshop. ACM, New York, pp 541-544.
19. Hu X, Zhang X, Lu C, Park EK, Zhou X (2009) Exploiting wikipedia as external knowledge for document clustering. In: Proceedings of the fifteenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 389-396.
20. Huang A, Milne D, Frank E, Witten I (2009) Clustering documents using a Wikipedia-based concept representation. In: Proceedings of the thirteenth Pacific-Asia conference on advances in knowledge discovery and data mining. Springer, Berlin, pp 628-636.
21. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3): 264-323.
22. Jing L, Ng M, Huang J (2010) Knowledge-based vector space model for text clustering. Knowl Inf Syst 25: 35-55.
23. Jolliffe I (2002) Principal component analysis. Springer, New York.
24. Karypis G (2003) CLUTO-a clustering toolkit. Technical Report #02-017, University of Minnesota, Department of Computer Science, Minnesota, MN, USA.
25. Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401: 788-791.
26. Lewis D (1999) Reuters-21578 text categorization test collection distribution 1. 0.
27. Meila M (2003) Comparing clusterings by the variation of information. In: Learning theory and Kernel Machines. Springer, Berlin, pp 173-187.
28. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11): 39-41.
29. Pessiot J-F, Kim Y-M, Amini MR, Gallinari P (2010) Improving document clustering in a learned concept space. Inf Process Manage 46(2): 180-192.
30. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11): 613-620.
31. Scholkopf B, Smola A, Muller K (1997) Kernel principal component analysis. Lect Notes Comput Sci 1327: 583-588.
32. Schütze H, Silverstein C (1997) Projections for efficient document clustering. In: Proceedings of the twentieth annual international ACM SIGIR conference on research and development in information retrieval, SIGIR '97. ACM, New York, pp 74-81.
33. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge.
34. Slonim N, Tishby N (2000) Document clustering using word clusters via the information bottleneck method. In: Proceedings of the twenty-third annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 208-215.
35. von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4): 395-416.
36. Wang P, Hu J, Zeng H, Chen Z (2009) Using wikipedia knowledge to improve text classification. Knowl Inf Syst 19(3): 265-281.
37. Wong SKM, Ziarko W, Wong PCN (1985) Generalized vector spaces model in information retrieval. In: Proceedings of the eighth annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 18-25.
38. Wu J, Xiong H, Chen J (2009) Adapting the right measures for k-means clustering. In: Proceedings of the fifteenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 877-886.
39. Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the twenty-sixth annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 267-273.
40. Zhao Y, Karypis G (2004) Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach Learn 55(3): 311-331.
41. Zhao Y, Karypis G (2005) Hierarchical clustering algorithms for document datasets. Data Min Knowl Discov 10(2): 141-168.