Mihail Motzev, Frank Lemke


Many problems exist in model building, identification, pattern recognition, approximation and extrapolation. To address these problems new techniques in data mining, like artificial neural networks, decision trees, genetic algorithms etc. have been developed and applied to analyze existing massive amounts of data and extract useful information. This paper presents a hybrid approach based on self-organizing data mining and concentrates on predictive models for business games and simulations. The results show that it is able to develop even complex models reliably and achieves lower overall error rates than state-of-the-art methods. The paper presents some of the results from international research done in Europe, Australia and most recently at Walla Walla University in College Place, Washington, USA.

Ключови думи

models; model-based simulations and business games; group method of data handling (GMDH); self-organizing data mining (SODM); multi-layer net of active neurons (MLNAN)


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