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

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First Case Study: Predicting Muscle Fatigue from EMG SignalsMost of the previous chapters discussed some application issues on a number of areas. This chapter discusses a case study in detail. The emphasis is on some comparative issues with other data mining techniques that do not use logic-based approaches. This chapter also provides a link to the data used in this study.

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Konrad Buczkowski,Pawe? Dziekański of interest for the purpose of gaining a better understanding of it. This system of interest might be artificial or natural. According to the Merriam-Webster online dictionary the term . is derived from the Greek terms . (plus, with, along with, together, at the same time) and . (to cause to stand)

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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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Algorithms and Computation in Mathematicsheoracle for classification and use that information to improve the understanding of the system under consideration. When the new example would unveil the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accu

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Haruka Mizuta,Takehiro Ito,Xiao Zhoust data. This accuracy can be a general description of how well the extracted model classifies test data. Some studies split thisaccuracy rate into two rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is on

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Edge Exploration of Temporal Graphsed from a type of analysis that extracts information from coincidence [ Blaxton andWestphal, 1998]. Sometimes called., this methodology allows a data analyst to discover correlations, or co-occurrences of transactional events. In the classic example, consider the items contained in a customer’s shop