Mining Textual Data

Introduction to Structured Document Clustering & ExtMiner

A lecture for TIES447 Data and Software Mining course, 18.11.2008.

Material

• Slides (pdf)
• Usage example
• ExtMiner distribution + datasets

Recommended reading

• M. Nurminen. Tiedonlouhinta rakenteisista dokumenteista, master's thesis (in Finnish), University of Jyväskylä, dept. of Mathematical Information Technology, 2005. http://urn.fi/URN:NBN:fi:jyu-200594 .

Information retrieval, indexing & search in general

• R. Baeza-Yates and B. Ribeiro-Neto: Modern Information Retrieval. Addison- Wesley, 1999. http://www.ischool.berkeley.edu/~hearst/irbook/
• C. D. Manning, P. Raghavan, and H. Schütze: Introduction to Information Retrieval, Cambridge University Press. 2008. http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html
• D. R. Cutting, D. R. Karger, J. O. Pedersen, and J.W. Tukey: Scatter/gather: a cluster-based approach to browsing large document collections. In N. Belkin, P. Ingwersen, A. M. Pejtersen, and E. A. Fox (eds.): Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 318–329. ACM Press, 1992. http://doi.acm.org/10.1145/133160.133214
• G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613–620, 1975. http://doi.acm.org/10.1145/361219.361220

Text & web mining in general

• S. Chakrabarti: Mining the Web - Discovering Knowledge from Hypertext Data. Morgan Kaufmann, 2003 http://www.cse.iitb.ac.in/~soumen/mining-the-web/
•  J. Dörre and P. Gerstl and R. Seiffert: Text mining: finding nuggets in mountains of textual data. In U. Fayyad, S. Chaudhuri, and D. Madigan (eds.): Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data 123 mining, pp. 398–401. ACM Press, 1999. http://doi.acm.org/10.1145/312129.312299
• M. A. Hearst: Untangling text data mining. In 37th Annual Meeting of the Association for Computational Linguistics, pp. 3–10. Morgan Kaufmann, 1999. http://acl.ldc.upenn.edu/P/P99/P99-1001.pdf
• Y. Kodratoff: Knowledge discovery in texts: A definition and applications. In Z. W. Ras and A. Skowron (eds.): Foundations of Intelligent Systems, 11th International Symposium, ISMIS ’99, Proceedings, vol. 1609 of Lecture Notes in Computer Science, pp. 16–29. Springer, 1999. http://citeseer.ist.psu.edu/kodratoff99knowledge.html
  

Document clustering, similarity measures

• E. Chávez and G. Navarro and R. Baeza-Yates and J. L. Marroquín: Searching in metric spaces. ACM Comput. Surv., 33(3):273–321, 2001. http://doi.acm.org/10.1145/502807.502808
• M. Cristo, P. Calado, E. S. de Moura, N. Ziviani1, and B. Ribeiro-Neto: Link information as a similarity measure in web classification. In G. Goos, J. Hartmanis, and J. van Leeuwen (eds.): String Processing and Information Retrieval, vol. 2857 of Lecture Notes in Computer Science, pp. 43–55. Springer, 2003. http://www.springerlink.com/link.asp?id=aryan7c17m1b94ta
• M. Ester, H.-P. Kriegel, J. Sander, and X. Xu: A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han, and U. Fayyad (eds.): Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD’96 ), pp. 226–231. AAAI Press, 1996. http://www.cs.ualberta.ca/~joerg/papers/KDD-96_final.pdf
• D. S. Modha and W. S. Spangler: Clustering hypertext with applications to web searching. In F. M. Shipman, III, P. J. Nürnberg, and D. L. Hicks (eds.): Proceedings of the eleventh ACM on Hypertext and hypermedia, pp. 143–152. ACM Press, 2000. http://doi.acm.org/10.1145/336296.336351
• A. Schenker, M. Last, H. Bunke, and A. Kandel: Comparison of distance measures for graph-based clustering of documents. In E. R. Hancock and M. Vento (eds.): Proceedings of the Graph Based Representations in Pattern Recognition, 4th IAPR International Workshop (GbRPR 2003), vol. 2726 of Lecture Notes in Computer Science, pp. 202–213. Springer, 2003. http://springerlink.metapress.com/link.asp?id=bxkjxj1vt9mlxylx
• A. Strehl: Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining. PhD thesis, The University of Texas at Austin, 2002. http://strehl.com/download/strehl-phd.pdf
• Y. Zhao and G. Karypis: Evaluation of hierarchical clustering algorithms for document datasets. In C. Nicholas, D. Grossman, K. Kalpakis, S. Qureshi, H. van Dissel, and L. Seligman (eds.): Proceedings of the Eleventh International Conference on Information and Knowledge Management. ACM Press, 2002, pp. 515–524. http://doi.acm.org/10.1145/584792.584877

Structured indexing & search

• S. Brin and L. Page: The anatomy of a large-scale hypertextual web search engine. In P. H. Enslow, Jr. and A. Ellis (eds.): Proceedings of the Seventh International Conference on World Wide Web 7. Elsevier, 1998. , pp. 107–117. http://dx.doi.org/10.1016/S0169-7552(98)00110-X
  
• E. A. Fox, G. L. Nunn, and W. C. Lee. Coefficients of combining concept classes in a collection. In Proc. of the 11th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pages 291–307. ACM Press, 1988. http://doi.acm.org/10.1145/62437.62465
• M. E. Frisse: Searching for information in a hypertext medical handbook. In J. B. Smith and F. Halasz (eds.): Proceeding of the ACM Conference on Hypertext, pp. 57–66. ACM Press, 1987. http://doi.acm.org/10.1145/317426.317433
• J. M. Kleinberg: Authoritative sources in a hyperlinked environment. In H. Karloff (ed.): Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algo- 127 rithms, pp. 668–677. Society for Industrial and Applied Mathematics, 1998. http://portal.acm.org/citation.cfm?id=315045
• R. W. Luk, H. Leong, T. S. Dillon, A. T. Chan, W. B. Croft, and J. Allan: A survey in indexing and searching XML documents. Journal of the American Society for Information Science and Technology, 53(6):415–437, 2002. http://dx.doi.org/10.1002/asi.10056
  
• M. Nurminen, A. Honkaranta, and T. Kärkkäinen. ExtMiner: Combining Multiple Ranking and Clustering Algorithms for Structured Document Retrieval. In International workshop on Integrating Data Mining, Databases and Information Retrieval (IDDI'05), 16th International Workshop on Database and Expert Systems Applications. IEEE, 2005. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1508411
• F. Weigel: A survey of indexing techniques for semistructured documents. Project thesis, Institute of Computer Science, LMU, Munich, 2002. http://www.pms.ifi.lmu.de/publikationen/#PA_Felix.Weigel
• K. Yang. Combining Text-, Link-, and Classification-based Retrieval Methods to Enhance Information Discovery on the Web. PhD thesis, University of North Carolina, 2002. http://www.webir.org/resources/phd/Yang_2002.pdf