ACCESSING DATA MINING RULES THROUGH EXPERT SYSTEMS

Basilis Boutsinas ()
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Basilis Boutsinas: Department of Business Administration, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26500 Rio, Patras, Greece

International Journal of Information Technology & Decision Making (IJITDM), 2002, vol. 01, issue 04, 657-672

Abstract: Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mining rules would not be very useful unless there are mechanisms to help analysts access them in a meaningful way. Actually, documenting and reporting the extracted knowledge is of considerable importance for the successful application of data mining in practice. In this paper, we propose a methodology for accessing data mining rules, which is based on using an expert system. We present how the different types of data mining rules can be transformed into the domain knowledge of any general-purpose expert system. Then, we present how certain attribute values given by the user as facts and/or goals can determine, through a forward and/or backward chaining, the related data mining rules. In this paper, we also present a case study that demonstrates the applicability of the proposed methodology.

Keywords: Data mining; expert systems (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622002000373

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