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Improving Effectiveness on Clickstream Data Mining

  • Cristina Wanzeller
  • Orlando Belo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

Developing and applying data mining processes are often very complex tasks to users without deep knowledge in this domain, particularly when such tasks involve clickstream data processing. One important and known challenge arises in the selection of mining methods to apply on a specific data analysis problem, trying to get better and useful results for a particular goal. Our approach to address this challenge relies on the reuse of the acquired experience from similar problems, which had provided successful mining processes in the past. In order to accomplish such goal, we implemented a prototype mining plans selection system, based on the Case-Based Reasoning paradigm. In this paper we explain how this paradigm and the implemented system may be explored to assist decisions on the data mining or Web usage mining specific scope. Additionally, we also identify the underlying issues and the approaches that were followed.

Keywords

Case Base Reasoning Mining Plan Improve Effectiveness Data Mining Process Data Mining Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations and Systems Approaches. Artificial Intelligence Communications (AICom) 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Aamodt, A.: Knowledge Acquisition and Learning by Experience - The Role of Case Specific Knowledge. In: Machine Learning and Knowledge Acquisition, Integrated Approaches, pp. 197–245. Academic Press, London (1995)Google Scholar
  3. 3.
    Ansari, S., Kohavi, R., Mason, L., Zheng, Z.: Integrating E-Commerce and Data Mining: Architecture and Challenges. In: Proc. 2001 IEEE International Conf. on Data Mining, pp. 27–34. IEEE Comput. Soc., Los Alamitos (2001)CrossRefGoogle Scholar
  4. 4.
    Apache Jakarta Tomcat (access, April 2006), http://tomcat.apache.org/
  5. 5.
    Bos, B.: W3C. Web Style Sheets – Home Page (access, April 2006), http://www.w3.org/Style/
  6. 6.
    Hilario, M., Kalousis, A.: Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS, vol. 2168, pp. 180–191. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Java 2 Platform, Standard Edition (J2SE). Sun Microsystems (access, April 2006), http://java.sun.com/javase/index.jsp
  8. 8.
    Java API for XML Processing (JAXP). Sun Microsystems (access, April 2006), http://java.sun.com/webservices/jaxp/
  9. 9.
    Java Database Connectivity, JDBC Data Access API. Sun Microsystems (access, April 2006), http://www.javasoft.com/products/jdbc/index.html
  10. 10.
    Java Server Pages. Sun Microsystems (access, April 2006), http://java.sun.com/products/jsp/
  11. 11.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufman, San Francisco (1993)Google Scholar
  12. 12.
    Koutri, M., Avouris, N., Daskalaki, S.: A Survey on Web Usage Mining Techniques for Web-Based Adaptive Hypermedia Systems. In: Chen, S.Y., Magoulas, G.D. (eds.) Adaptable and Adaptive Hypermedia Systems, Idea Publishing Inc., Hershey (2005)Google Scholar
  13. 13.
    Lindner, G., Studer, R.: AST: Support for algorithm selection with a CBR approach. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS, vol. 1704, pp. 418–423. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  14. 14.
    MetaL project (access, April 2006), http://www.metal-kdd.org/
  15. 15.
    Mobasher, B., Berendt, B., Spiliopoulou, M.: KDD for Personalization. In: PKDD 2001 Tutorial (2001)Google Scholar
  16. 16.
    Morik, K., Scholz, M.: The MiningMart Approach to Knowledge Discovery in Databases. In: Zhong, N., Liu, J. (eds.) Intelligent Technologies for Information Analysis. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    Predictive Model Markup Language. Data Mining Group (access, April 2006), http://www.dmg.org/index.html
  18. 18.
    Richter, M.: The Knowledge Contained in Similarity Measures. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010. Springer, Heidelberg (1995)Google Scholar
  19. 19.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Hillsdale (1989)Google Scholar
  20. 20.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations 1(2), 1–12 (2000)CrossRefGoogle Scholar
  21. 21.
    W3C HTML Working Group. HyperText Markup Language (HTML) – Home Page (access, April 2006), http://www.w3.org/MarkUp/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cristina Wanzeller
    • 1
  • Orlando Belo
    • 2
  1. 1.Departamento de Informática, Instituto Superior Politécnico de ViseuEscola Superior de Tecnologia de ViseuViseuPortugal
  2. 2.Departamento de InformáticaEscola de Engenharia, Universidade do MinhoBragaPortugal

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