Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions

  • Evangelos Triantaphyllou
Part of the Springer Optimization and Its Applications book series (SOIA, volume 43)


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 [ Torvik and Triantaphyllou, 2002; 2003; 2004; 2006].


Boolean Function Query Complexity Conjunctive Normal Form Inference Algorithm Inference Process 
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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceLouisiana State UniversityBaton RougeUSA

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