Foundations of Rule Learning

• Main Authors: Fürnkranz, Johannes (Author), Gamberger, Dragan (Author), Lavrač, Nada (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
• Series: Cognitive Technologies,
• Subjects: Computer science.; Computers.; Data mining.; Artificial intelligence.; Pattern recognition.; Statistics.; Computer Science.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Pattern Recognition.; Computation by Abstract Devices.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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 machin...

Table of Contents:

• 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.