Decision making with uncertainty and data mining

Authors:
David L. Olson;Desheng Wu
Affiliations:
Department of Management, University of Nebraska, Lincoln, NE;Department of Management, University of Nebraska, Lincoln, NE
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

Citing 4
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Abstract

Data mining is a newly developed and emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. It is expected to offer more and more support to modern organizations which face a serious challenge of how to make decision from massively increased information so that they can better understand their markets, customers, suppliers, operations and internal business processes. This paper discusses fuzzy decision-making using the Grey Related Analysis method. Fuzzy models are expected to better reflect decision maker uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo Simulation, a data mining technique, is used to measure the impact of fuzzy models relative to crisp models. Fuzzy models were found to provide fits as good as crisp models in the data analyzed.