Table of contents

  1. Front Matter
    Pages i-xvii
  2. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 1-17
  3. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 19-55
  4. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 57-63
  5. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 65-93
  6. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 95-112
  7. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 113-133
  8. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 135-169
  9. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 171-186
  10. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 187-216
  11. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 217-246
  12. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 247-265
  13. Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač
    Pages 267-298
  14. Back Matter
    Pages 299-334

About this book

Introduction

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.

Keywords

Association rule learning Classification rule induction Propositional rule learning Relational data mining Subgroup discovery

Authors and affiliations

  • Johannes Fürnkranz
    • 1
  • Dragan Gamberger
    • 2
  • Nada Lavrač
    • 3
  1. 1.FB InformatikTU DarmstadtDarmstadtGermany
  2. 2.Rudjer Bošković InstituteZagrebCroatia
  3. 3., Dept. Knowledge TechnologiesJožef Stefan InstituteLjubljanaSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-75197-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-75196-0
  • Online ISBN 978-3-540-75197-7
  • Series Print ISSN 1611-2482
  • About this book