Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
The Needles-in-Haystack Problem
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Interfaces
Optimization algorithm for learning consistent belief rule-base from examples
Journal of Global Optimization
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There are many approaches to data mining and knowledge discovery (DM&KD), including neural networks, closest neighbor methods, and various statistical methods. This monograph, however, focuses on the development and use of a novel approach, based on mathematical logic, that the author and his research associates have worked on over the last 20 years. The methods presented in the book deal with key DM&KD issues in an intuitive manner and in a natural sequence. Compared to other DM&KD methods, those based on mathematical logic offer a direct and often intuitive approach for extracting easily interpretable patterns from databases. The book discusses the theoretical foundations of the methods described, and it also presents a wide collection of examples, many of which come from real-life applications. Almost all theoretical developments are accompanied by extensive empirical analysis which often involved the solution of a very large number of simulated test problems.