This book reviews the basics of rule learning as applied to classical machine learning and modern data mining. It connects attribute-value learning with inductive logic programming, and offers complete coverage of most important elements of rule learning.

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.

Part I. Introduction to Rule Learning.- Machine Learning and Data Mining.- Propositional Rule Learning.- Relational Rule Learning.- Part II. Elements of Rule Learning.- Formal Framework for Rule Analysis.- Features.- Heuristics.- Pruning of Rules and Rule Sets.- Survey of Classification Rule Learning Systems Through the Analysis of Rule Learning Elements Used.- Part III. Selected Topics in Predictive Induction.- Part IV Selected Techniques and Applications.