Titlebook: Data Mining and Knowledge Discovery via Logic-Based Methods; Theory, Algorithms, Evangelos Triantaphyllou Book 2010 Springer Science+Busin

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Introduction

Data Mining and Knowledge Discovery via Logic-Based Methods978-1-4419-1630-3Series ISSN 1931-6828 Series E-ISSN 1931-6836

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Immune Therapy of Human Cancersng examples. This algorithm is an extension of the B&B algorithm described in the previous chapter. Now the states of the search space are described by using more information and this seems to be critical in leading to good search results faster. This chapter is based on the developments first presented in [ Triantaphyllou, 1994].

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Robert Peter Gale MD, PhD,Anna Butturinior inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).

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Recent Advances of Palindromic Factorizationets of malignant andbenign cases. We applied theOCAT approach, as it is embedded in the RA1 heuristic (see also Chapter 4), after the data were transformed into binary ones according to the method described in Section 2.2. The following sections describe the data and inferreddiagnostic rules in more detail.

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Lecture Notes in Computer Sciencees some comprehensive concluding remarks. As was mentioned earlier, there are many approaches todata mining andknowledge discovery from data sets. Such approaches includeneural networks, closest neighbor methods, and various statistical methods. However, such approaches may have some severe limitations for a number of reasons.

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Data Mining and Knowledge Discovery via Logic-Based MethodsTheory, Algorithms,