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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations and Systems Approaches. Artificial Intelligence Communications (AICom) 7(1), 39–59 (1994)
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)
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)
Apache Jakarta Tomcat (access, April 2006), http://tomcat.apache.org/
Bos, B.: W3C. Web Style Sheets – Home Page (access, April 2006), http://www.w3.org/Style/
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)
Java 2 Platform, Standard Edition (J2SE). Sun Microsystems (access, April 2006), http://java.sun.com/javase/index.jsp
Java API for XML Processing (JAXP). Sun Microsystems (access, April 2006), http://java.sun.com/webservices/jaxp/
Java Database Connectivity, JDBC Data Access API. Sun Microsystems (access, April 2006), http://www.javasoft.com/products/jdbc/index.html
Java Server Pages. Sun Microsystems (access, April 2006), http://java.sun.com/products/jsp/
Kolodner, J.: Case-Based Reasoning. Morgan Kaufman, San Francisco (1993)
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)
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)
MetaL project (access, April 2006), http://www.metal-kdd.org/
Mobasher, B., Berendt, B., Spiliopoulou, M.: KDD for Personalization. In: PKDD 2001 Tutorial (2001)
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)
Predictive Model Markup Language. Data Mining Group (access, April 2006), http://www.dmg.org/index.html
Richter, M.: The Knowledge Contained in Similarity Measures. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010. Springer, Heidelberg (1995)
Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Hillsdale (1989)
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)
W3C HTML Working Group. HyperText Markup Language (HTML) – Home Page (access, April 2006), http://www.w3.org/MarkUp/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wanzeller, C., Belo, O. (2006). Improving Effectiveness on Clickstream Data Mining. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_13
Download citation
DOI: https://doi.org/10.1007/11790853_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
Online ISBN: 978-3-540-36037-7
eBook Packages: Computer ScienceComputer Science (R0)