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Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions

In: Data Mining and Knowledge Discovery via Logic-Based Methods

Author

Listed:
  • Evangelos Triantaphyllou

    (Louisiana State University)

Abstract

In all previous discussions the problem was how to infer a general Boolean function based on some training examples. Such a Boolean function can be completely inferred if all possible binary examples (states) in the space of the attributes are used for training. Thus, one may never be 100% certain about the validity of the inferred knowledge when the number of training examples is less than 2 n . The situation is different, however, if one deals with the inference of systems that exhibit monotonic behavior. The developments presented in this chapter are based on the award-winning doctoral work of Vetle I. Torvik and in particular on the research results first published in [ aut Torvik, V.I. Torvik and aut Triantaphyllou, E. Triantaphyllou, 2002; 2003; 2004; 2006].

Suggested Citation

  • Evangelos Triantaphyllou, 2010. "Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions," Springer Optimization and Its Applications, in: Data Mining and Knowledge Discovery via Logic-Based Methods, chapter 0, pages 191-227, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-1630-3_10
    DOI: 10.1007/978-1-4419-1630-3_10
    as

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