Foundations of Rule Learning By Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
English | PDF | 2012 | 345 Pages | ISBN : 3540751963 | 9.86 MB
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.