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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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
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