Advertisement

Pattern Taxonomy Mining for Information Filtering

  • Xujuan Zhou
  • Yuefeng Li
  • Peter Bruza
  • Yue Xu
  • Raymond Y. K. Lau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)

Abstract

This paper examines a new approach to information filtering by using data mining method. This new model consists of two components, namely, topic filtering and pattern taxonomy mining. The aim of using topic filtering is to quickly filter out irrelevant information based on the user profiles. The aim of applying pattern taxonomy mining techniques is to rationalize the data relevance on the reduced data set. Our experiments on Reuters RCV1(Reuters Corpus Volume 1) data collection show that more effective and efficient information access has been achieved by combining the strength of information filtering and data mining method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  2. 2.
    Li, Y., Zhang, C., Swan, J.R.: An information filtering model on the web and its application in jobagent. Knowl.-Based Syst. 13(5), 285–296 (2000)CrossRefGoogle Scholar
  3. 3.
    Robertson, S.E., Soboroff, I.: The trec 2002 filtering track report. In: TREC (2002)Google Scholar
  4. 4.
    Xu, Y., Li, Y.: Generating concise association rules. In: CIKM, pp. 781–790 (2007)Google Scholar
  5. 5.
    Wu, S.T., Li, Y., Xu, Y.: Deploying approaches for pattern refinement in text mining. In: ICDM, pp. 1157–1161 (2006)Google Scholar
  6. 6.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  7. 7.
    Mostafa, J., Mukhopadhyay, S., Lam, W., Palakal, M.J.: A multilevel approach to intelligent information filtering: Model, system, and evaluation. ACM Trans. Inf. Syst. 15(4), 368–399 (1997)CrossRefGoogle Scholar
  8. 8.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Feldman, R., Aumann, Y., Amir, A., Zilberstein, A., Klösgen, W.: Maximal association rules: A new tool for mining for keyword co-occurrences in document collections. In: KDD, pp. 167–170 (1997)Google Scholar
  10. 10.
    Tzvetkov, P., Yan, X., Han, J.: Tsp: Mining top- closed sequential patterns. Knowl. Inf. Syst. 7(4), 438–457 (2005)CrossRefGoogle Scholar
  11. 11.
    Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Eng. 11(5), 798–804 (1999)CrossRefGoogle Scholar
  12. 12.
    Li, Y., Zhong, N.: Mining ontology for automatically acquiring web user information needs. IEEE Transactions on Knowledge and Data Engineering 18(4), 554–568 (2006)CrossRefGoogle Scholar
  13. 13.
    Yao, Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. International Journal of Man-Machine Studies 37(6), 793–809 (1992)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Zhong, N.: Web mining model and its applications for information gathering. Knowledge-Based Systems 17, 207–217 (2004)CrossRefGoogle Scholar
  15. 15.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: SIGIR, pp. 42–49 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xujuan Zhou
    • 1
  • Yuefeng Li
    • 1
  • Peter Bruza
    • 1
  • Yue Xu
    • 1
  • Raymond Y. K. Lau
    • 2
  1. 1.Faculty of Information TechnologyQueensland University of TechnologyBrisbaneAustralia
  2. 2.Department of Information SystemsCity University of Hong KongHong KongChina

Personalised recommendations