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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Olson, D.L., Wu, D. (2005). Decision Making with Uncertainty and Data Mining. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_1
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DOI: https://doi.org/10.1007/11527503_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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