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.