Abstract
The use of Data Mining in removing current bottlenecks within Case-based Reasoning (CBR) systems is investigated along with the possible role of CBR in providing a knowledge management back-end to current Data Mining systems. In particular, this paper discusses the use of Data Mining in two aspects of the MZ system [ANAN97a], namely, the acquisition of cases and discovery of adaptation knowledge. We discuss, in detail, the approach taken to discover cases and outline the methodology to discover adaptation knowledge. For case discovery, a Kohonen network is used to identify initial clusters within the database. These clusters are then analysed using C4.5 and non-unique clusters are grouped to form concepts. A regression tree induction algorithm is then used to ensure that the concepts are rich in information required to predict the dependent variable in the data set. Cases are then chosen from each of the identified concepts as well as outliers in the database. Initial results obtained in the acquisition of cases are presented and analysed. They indicate that the proposed approach achieves a high reduction in the size of the case base.
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© 1998 Springer-Verlag Berlin Heidelberg
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Anand, S.S., Patterson, D., Hughes, J.G., Bell, D.A. (1998). Discovering case knowledge using data mining. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_3
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DOI: https://doi.org/10.1007/3-540-64383-4_3
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