SELF-ORGANIZING DATA MINING TECHNIQUES IN MODEL BASED SIMULATION GAMES FOR BUSINESS TRAINING AND EDUCATION

Mihail Motzev, Frank Lemke

Анотация


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)

Източници


Elgood, Ch., 2005. Using Management Games. Burlington, USA. Ashgate Publishing, p.10.

Luhn, H.P., 1958. A Business Intelligence System. IBM Technical Journals, Volume 2, Number 4, p. 314.

Power D.J. (ed.). 2007. A Brief History of Decision Support Systems (ver. 4.0). Decision Support Systems Resources, p. 6. Available at: < http://dssresources.com/history/dsshistory.html.

Davenport, T. and Harris, J., 2007. Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press, March 2007, pp. 7-8.

Beller, M. and Barnett, A., 2009. Next Generation Business Analytics Technology Trends. Lightship Partners LLC, p. 5. Available at: < (http://www.docstoc.com/docs/7486045/Next-Generation-Business-Analytics-Technology-Trends).

Shmueli, G. and others, 2007. Predictive vs. Explanatory Modeling. IS Research, Conference on Information Systems and Technology, Seattle, WA, November 3-4, Available at: < (http://www.citi.uconn.edu/cist07/5c.pdf).

Nyce, Ch., 2007. Predictive Analytics. White Paper, American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America, p. 1.

Turban, E. and Aronson, J., 2001. Decision Support Systems and Intelligent Systems. Prentice-Hall, p. 148.

Berry, M. and Linoff, G., 2000. Mastering Data Mining. Wiley, p. 8.

Lucey, T., 1991, Management Information Systems. DP Publications Lim., 6th ed., p. 16.

Fayyad, U., Piatetsky-Shapiro, G., and Smyth P., 1996. From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence Magazine, Fall 1996, pp. 37-54. Available at: (http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf).

Marakas, G., 2003. Decision Support Systems in the 21st Century. Prentice-Hall, p.328.

White paper, 2005. An Architecture for Enterprise Business Intelligence. MicroStrategy, Available at: http://www.microstrategy.com/Publications/Whitepapers.

Mueller, J-A. and Lemke, F., 2003. Self-Organizing Data Mining: An Intelligent Approach To Extract Knowledge From Data. Trafford Publishing, Canada.

Ivakhnenko, A. G., 1966. Group Method of Data Handling - A Rival of the Method of Stochastic Approximation. Soviet Automatic Control, No.13, pp. 43-71.

Stone, M., 1977. Asymptotics for and against cross-validation. Biometrika 64 (1) pp. 29–35.

Madala, H. and Ivakhnenko, A.G., 1994. Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton, FL, USA.

Marchev, A. and Motzev, M., 1985. Computer Macroeconomic Models for Simulation Experiments. Systems Analysis and Simulation, Berlin, Band 28, 1985 II, pp.145-150.

Marchev, A., Motzev M., and Muller, J-A., 1985. Applications of The Self-Organization Procedures for Business System Models Building. Automatics, Kiev, Vol. 1, 1985, pp. 37-44.

Marchev, A. and Motzev, M., 1989. Principles of Multi-Stage Selection in Software Development in Decision Support Systems. Methodology and Software for Interactive Decision Support (Lecture Notes in Economics and Mathematical Systems), Springer-Verlag, 337 IIASA, pp. 181-189.

Motzev, M., and Marchev, A., 1984. Applications of Management Simulation Games for Student Training in Business Education, X Internationales Seminar Uber Rechnergestutzte Planspiele, Berlin.

Motzev, M., and Marchev, A., 1988. Multi-Stage Selection Algorithms in Simulation, Proceedings of XII IMACS World Congress, Paris, France: vol. 4, July, pp. 533-535.

Motzev, M., and Marchev, A., 1991. Macroeconomic Models for Simulation of the Bulgarian Economy. Systems Analysis, Models and Simulation, vol. 8.

Motzev, M., Marchev, A., and Muller, J-A., 1986. Modeling and Forecasting on Macro-Economic Systems Using Auto-Regressive Models., Social Management, Sofia, vol 6, pp. 77-93.

Motzev M., 1985. A New Approach for Simulation Models Building. XVI IFAC/ISSAGA Workshop, Alma-Ata, June.

Motzev, M., 2012. New Product – An Integrated Simulation Game in Business Education. Bonds & Bridges, Proceedings of the World Conference of the ISAGA, pp. 63-75.

Onwubolu, G. (ed.), 2009. Hybrid Self-Organizing Modeling Systems. Springer-Verlag Berlin Heidelberg.

Sheskin, D., 2011. Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, FL: CRC Press, p. 109.




##submission.copyrightStatement##

##submission.license.cc.by-nc-nd4.footer##