Relational Features
While typical data mining approaches find patterns/models from data stored in a single data table, relational data mining and inductive logic programming approaches (Džeroski & Lavrač, 2001; Lavrač & Džeroski, 1994a) find patterns/models from data
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Johannes F¨urnkranz Dragan Gamberger Nada Lavraˇc
Foundations of Rule Learning
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Johannes F¨urnkranz FB Informatik TU Darmstadt Darmstadt Germany
Nada Lavraˇc Department of Knowledge Technologies Joˇzef Stefan Institute Ljubljana Slovenia
Dragan Gamberger Rudjer Boˇskovi´c Institute Zagreb Croatia
Managing Editors Prof. Dov M. Gabbay Augustus De Morgan Professor of Logic Department of Computer Science King’s College London Strand, London, UK
Prof. Dr. J¨org Siekmann Forschungsbereich Deduktions- und Multiagentensysteme, DFKI Saarbr¨ucken, Germany
Cognitive Technologies ISSN 1611-2482 ISBN 978-3-540-75196-0 ISBN 978-3-540-75197-7 (eBook) DOI 10.1007/978-3-540-75197-7 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012951145 ACM Codes: I.2, H.2 c Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
It was on a balmy spring day in Nanjing, while we were working on a paper on contrast discovery for the Journal of Machine Learning Research, that Nada Lavraˇc let slip that she was writing a book on rule learning with Johannes F¨urnkranz and Dragan Gamberger. I must admit that I was initially skeptical. Rule learning i
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