Abstract
Data mining techniques hold great promise for enabling the automatic analysis of large data sets generated by scientific simulation, and thus, may help engineers and scientists unravel the causal relationships in the underlying system. In this chapter, we propose several data modeling methods to incorporate spatial and temporal features of scientific simulation data and investigate some of them in the context of developing models for predicting burst events in turbulent flow. We use the classification rules algorithm C4.5rules and support-vector machines on the turbulent flow simulation data to develop predictive models for identifying upward or downward velocity movements of the flow close to the wall as a function of swirl strength in the nearby region.
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References
L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Monterrey, CA, 1984.
W.W. Cohen. Fast effective rule induction. In Proc. of the Twelfth International Conference on Machine Learning, 1995.
C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273–297, 1995.
Mukund Deshpande and George Karypis. Selective markov models for predicting web-page accesses. In First Siam International Conference on Data Mining, 2001.
D.J. Spiegelhalter D. Michie and C.C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.
E.H. Han, G. Karypis, and V. Kumar. Text categorization using weight adjusted k-nearest neighbor classification. In David Wai-Lok Cheung, Graham J. Williams, and Qing Li, editors, PAKDD, volume 2035 of Lecture Notes in Computer Science, pages 53–65. Springer, 2001.
T. Joachims. Text categorization with support vector mar chines: Learning with many relevant features. In Proc. of the European Conference on Machine Learning,1998.
I. Marusic, G.V. Candler, V. Interrante, P.K. Subbareddy, and A. Moss. Real time feature extraction for the analysis of turbulent flows. In R. Grossman, C. Kamath, Ph. Kegelmeyer, V. Kumar, and R. Namburu, editors, Data Mining for Scientific and Engineering Applications. Kluwer, 2001.
J. Ross Quinlan. C4–5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
Y. Yang and J. Pederson. A comparative study on feature selection in text categorization. In Proc. of the Fourteenth International Conference on Machine Learning, 1997.
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Han, EH., Karypis, G., Kumar, V. (2001). Data Mining for Turbulent Flows. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_14
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_14
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-0114-7
Online ISBN: 978-1-4615-1733-7
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