Knowledge Discovery in Databases mit Neuro-Fuzzy-Systemen. Entwurf für einen integrierten Ansatz zum Data Mining in betrieblichen Datenbanken
AbstractThe new field of research called Knowledge Discovery in Databases (KDD) aims at tearing down the last barrier in enterprises' information flow, the data analysis step. This is done by developing and integrating data mining algorithms. A neuro-fuzzy-system can be such a data mining tool. After introducing the KDD process and differentiating it from data mining we explain the basics of neuro-fuzzy-systems. As one possible system we pick out the NEFCLASS architecture. After that we examine the appropriateness of such a neuro-fuzzy-system for data mining. Starting out from the problem areas identified, we develop a concept for an integrated KDD system based on NEFCLASS. One part of this concept, the solution of the missing-values-problem deserves closer examination. To this end we introduce different imputation algorithms and test them on real data. Finally, we propose a draft for an implementation of the overall concept worked out before.
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Bibliographic InfoPaper provided by Friedrich-Schiller-Universität Jena, Wirtschaftswissenschaftliche Fakultät in its series Working Paper Series A with number 1998-07.
Date of creation: 25 Apr 1998
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