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PAKDD
1998
ACM

Discovering Case Knowledge Using Data Mining

9 years 4 months ago
Discovering Case Knowledge Using Data Mining
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 M2 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...
Sarabjot S. Anand, David W. Patterson, John G. Hug
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where PAKDD
Authors Sarabjot S. Anand, David W. Patterson, John G. Hughes, David A. Bell
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