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An efficient global discretization method

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Research and Development in Knowledge Discovery and Data Mining (PAKDD 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1394))

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References

  1. Fayyad, U.M., & Irani, K.B. (1993). Multi-interval discretization of continuous-value attributes for classification learning. In Proc. 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. San Francisco: Morgan Kaufmann.

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  2. Ho, K.M., & Scott, P.D. (1997a). Zeta: A global method for discretization of continuous variables. In KDD97: 3rd International Conference of Knowledge Discovery and Data Mining, pp. 191–194, Newport Beach, CA.

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  3. Ho, K.M., & Scott, P.D. (1997b). An Efficient Global Discretization Method. Technical Report CSM-296, Department of Computer Science, University of Essex, England.

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  4. Kohavi, R., John, G., Long, R., Manley, D. & Pfleger, K. (1994), MLC++: A machine learning library in C++. In Tools with Artificial Intelligence, IEEE Computer Society Press, pp. 740–743.

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  5. Quinlan, J.R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4 pp. 77–90.

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© 1998 Springer-Verlag Berlin Heidelberg

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Ho, K.M., Scott, P.D. (1998). An efficient global discretization method. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_35

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  • DOI: https://doi.org/10.1007/3-540-64383-4_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64383-8

  • Online ISBN: 978-3-540-69768-8

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