Krajem 2011. godine iz štampe izlazi dugo godina pripremana knjiga. Napisana je u suradnji sa kolegama iz Njemačke i Slovenije i predstavlja rezultat suradnje na više zajedničkih projekata. Prvi dio predstavlja prikaz osnovnih pristupa indukciji pravila dok drugi detaljno opisuje svaki od sastavnih dijelova tog procesa. Knjiga daje vrlo detaljan prikaz postojećih sustava zasnovanih na učenju pravila te neke važne primjene u otkrivanju znanja. Uključen je vrlo detaljan pregled literature.

Sažetak knjige:

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.   The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.