Abstract
In this paper we introduce an integrated framework for Data Mining and Knowledge Management and show how Knowledge Management can complement Data Mining. Specifically, we examine methods how to improve the knowledge intensive and weak-structured process of Data Mining (DM) through the use of an Experience Factory and the method of Case Base Reasoning.
The paper is divided into two sections: In the first section, we explain how knowledge and experience made in Data Mining can be used for following DM-projects and why it is therefore important to manage the creation, capture, organization and reuse of Data Mining experience. We then analyze a DM-process model, here CRISP-DM [10], and identify how knowledge and experience of this process can be captured and reused. In the second step, we describe our approach to support the DM-process through methods of Case Based Reasoning within an Experience Factory.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI-Communications 7(1), 39–59 (1994)
Althoff, K.-D., Nick, M., Tautz, C.: Concepts for reuse in the experience factory and their implementation for CBR system development. In: Proceedings of the Eleventh German Workshop on Machine Learning (August 1998)
Bartlmae, K.: An Experience Factory Approach for Data Mining. In: Proceedings of the second Workshop: Data Mining und Data Warehousing als Grundlage Entscheidungsunterstützender Systeme (DMDW 1999), Univ. Magdeburg (September 1999)
Basili, V.R., Caldiera, G., Rombach, H.D.: Experience Factory. In: Marciniak, J.J. (ed.) Encyclopedia of Software Engineering, vol. 1, pp. 528–532. John Wiley and Sons, Chichester (1994)
Berry, M.J.A., Linhoff, G.: Data Mining Techniques. For Marketing, Sales and Customer Support. Wiley Computer Publication, Chichester (1997)
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., Zanasi, A.: Discovering Data Mining. From Concept To Implementaion. Prentice Hall, Upper Saddle River (1998)
Cho, J.R., Mathews, R.C.: Interactions Between Mental Models Used in Categorization and Experiential Knowledge of Specific Cases. The Journal of Experimental Psychology 49A(3) (1996)
CRISP-DM, DaimlerChrysler, Forschung und Technologie (March 1999), http://www.ncr.dk/CRISP
Engels, R., Lindner, G., Studer, R.: A Guided Tour through the Data Mining Jungle. In: Proceedings of the 3rd International Conference on Knowledge Discovery in Databases (KDD-1997), Newport Beach, CA (August 1997)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Form Data Mining to Knowledge Discovery: An Overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge (1995)
Gresse von Wangenheim, C., Moraes, A.R., Althoff, K.-D., Barcia, R.M., Weber, R., Martins, A.: Case-Based Reasoning Approach to Reuse of Experiential Knowledge in Software Measurement Programs. In: Proc. of the 6th German Workshop on Case-Based Reasoning, Berlin, Germany (1998)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, neural and statistical Classification, Ellis Horwood (1994)
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Bartlmae, K. (1999). Optimizing Data-Mining Processes: A CBR Based Experience Factory for Data Mining. In: Hui, L.C.K., Lee, DL. (eds) Internet Applications. ICSC 1999. Lecture Notes in Computer Science, vol 1749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46652-9_3
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DOI: https://doi.org/10.1007/978-3-540-46652-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66903-6
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