Lecture Notes in Computer Science, Professional Knowledge Management: Third Biennial Conference, WM 2005, Kaiserslautern, Germany, April 10-13, 2005, Revised Selected Papers vol:3782 pages:483-495
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.