From knowledge discovery to implementation: a business intelligence approach using neural network rule extraction and decision tables

Authors:
Christophe Mues;Bart Baesens;Rudy Setiono;Jan Vanthienen
Affiliations:
School of Management, University of Southampton, Southampton, United Kingdom;School of Management, University of Southampton, Southampton, United Kingdom;Dept. of Information Systems, Kent Ridge, National University of Singapore, Singapore, Republic of Singapore;Dept. of Applied Economic Sciences, K.U.Leuven, Leuven, Belgium
Venue:
WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
Year:
2005

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Abstract

The advent of knowledge discovery in data (KDD) technology has created new opportunities to analyze huge amounts of data. However, in order for this knowledge to be deployed, it first needs to be validated by the end-users and then implemented and integrated into the existing business and decision support environment. In this paper, we propose a framework for the development of business intelligence (BI) systems which centers on the use of neural network rule extraction and decision tables. Two different types of neural network rule extraction algorithms, viz. Neurolinear and Neurorule, are compared, and subsequent implementation strategies based on decision tables are discussed.