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Boolean Function Complementation Based Algorithm for Data Discretization

  • Grzegorz Borowik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8112)

Abstract

This paper presents a fast algorithm for discretization of decision tables. An important novelty of the proposed solution is the application of the original algorithm of Boolean function complementation, which is a basic procedure of the field of logic synthesis, in the process of discretizing the data. This procedure has already been used by the author to calculate reducts of decision tables, where the time of calculation has been significantly reduced. It yields the idea of using the algorithm of complementation in the process of discretization. The algorithm has been generalized for the discretization of inconsistent decision tables and is used in the processing of numerical data from various fields of technology, especially for multimedia data.

Keywords

discretization quantization data mining Boolean function complementation telecommunications biomedical engineering 

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References

  1. 1.
    Borowik, G.: Data mining approach for decision and classification systems using logic synthesis algorithms. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol. 6, pp. 3–23. Springer International Publishing (2014), doi:10.1007/978-3-319-01436-4_1Google Scholar
  2. 2.
    Borowik, G., Łuba, T.: Fast algorithm of attribute reduction based on the complementation of boolean function. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol. 6, pp. 25–41. Springer International Publishing (2014), doi:10.1007/978-3-319-01436-4_2Google Scholar
  3. 3.
    Borowik, G., Łuba, T., Zydek, D.: Features reduction using logic minimization techniques. International Journal of Electronics and Telecommunications 58(1), 71–76 (2012)CrossRefGoogle Scholar
  4. 4.
    Brayton, R.K., Hachtel, G.D., McMullen, C.T., Sangiovanni-Vincentelli, A.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers (1984)Google Scholar
  5. 5.
    Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehericy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage 56(2), 766–781 (2011), doi:10.1016/j.neuroimage.2010.06.013CrossRefGoogle Scholar
  6. 6.
    Dasgupta, S., Papadimitriou, C.H., Vazirani, U.V.: Algorithms. McGraw-Hill (2008)Google Scholar
  7. 7.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial (1999)Google Scholar
  8. 8.
    Łuba, T., Rybnik, J.: Rough sets and some aspects in logic synthesis. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)Google Scholar
  9. 9.
    Mangasarian, O.L., Wolberg, W.H.: Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)Google Scholar
  10. 10.
    Papadimitriou, C.H.: Computational complexity. Academic Internet Publ. (2007)Google Scholar
  11. 11.
    Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)Google Scholar
  12. 12.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)Google Scholar
  13. 13.
    Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Symposium on Computer Applications and Medical Care, pp. 261–265. IEEE Computer Society Press (1988)Google Scholar
  14. 14.
    Žádník, M., Michlovský, Z.: Is Spam Visible in Flow-Level Statistics? Tech. rep., CESNET National Research and Education Network (2009), http://www.fit.vutbr.cz/research/view_pub.php?id=9277
  15. 15.
    UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Grzegorz Borowik
    • 1
  1. 1.Institute of TelecommunicationsWarsaw University of TechnologyWarsawPoland

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