Bisociative Knowledge Discovery An Introduction to Concept, Algo
Modern knowledge discovery methods enable users to discover complex patterns of various types in large information repositories. However, the underlying assumption has always been that the data to which the methods are applied originates from one domain.
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LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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Michael R. Berthold (Ed.)
Bisociative Knowledge Discovery An Introduction to Concept, Algorithms, Tools, and Applications
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editor Michael R. Berthold University of Konstanz Department of Computer and Information Science Konstanz, Germany E-mail: [email protected] Acknowledgement and Disclaimer The work reported in this book was funded by the European Commission in the 7th Framework Programme (FP7-ICT-2007-C FET-Open, contract no. BISON-211898).
ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-31829-0 e-ISBN 978-3-642-31830-6 DOI 10.1007/978-3-642-31830-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2012941862 CR Subject Classification (1998): I.2, H.3, H.2.8, H.4, C.2, F.1 LNCS Sublibrary: SL 7 – Artificial Intelligence © The Editor(s) (if applicable) and the Author(s) 2012. The book is published with open access at SpringerLink.com. OpenAccess. This book is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. All commercial rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. 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 commercial use must always be obtained from Springer. Permissions for commercial 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, 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. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
We have all heard of the success story of the discovery of a link between the mental problems of children and the chemical pollutants in their drinking water. Similarly, we have heard of the 1854 Broad Street cholera outbreak in London, and the linking of it to a contaminated public water pump. These are two highprofile examples of bisociation, the combination of information from two different sources. This is exactly the focus
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