Bisociative Knowledge Discovery_ An Introduction to Concept, Algorithms, Tools, and Applications [Berthold 2012-07-05].pdf

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Lecture Notes in Artificial Intelligence
Subseries of Lecture Notes in Computer Science
LNAI Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
7250
LNAI Founding Series Editor
Joerg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
http://avaxhome.ws/blogs/ChrisRedfield
Michael R. Berthold (Ed.)
Bisociative
Knowledge Discovery
An Introduction to Concept, Algorithms,
Tools, and Applications
13
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: michael.berthold@uni-konstanz.de
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 Springer-
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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 high-
profile examples of bisociation, the combination of information from two different
sources.
This is exactly the focus of the BISON project and this book. Instead of
attempting to keep up with the meaningful annotation of the data floods we are
facing, the BISON group pursued a network-based integration of various types
of data repositories and the development of new ways to analyze and explore the
resulting gigantic information networks. Instead of finding well-defined global or
local patterns they wanted to find domain-bridging associations which are, by
definition, not well defined since they will be especially interesting if they are
sparse and have not been encountered before.
The present volume now collects the highlights of the BISON project. Not
only did the consortium succeed in formalizing the concept of bisociation and
proposing a number of types of bisociation and measures to rank their “bisociative-
ness,” but they also developed a series of new algorithms, and extended several
of the existing algorithms, to find bisociation in large bisociative information
networks.
From a personal point of view, I was delighted to see that some of our own
work on finding structurally similar pieces in large networks actually fit into that
framework very well: Random walks, and related diffusion-based methods, can
help find correlated nodes in bisociative networks. The concept of bisociative
knowledge discovery formalizes an aspect of data mining that people have been
aware of to some degree but were unable to formally pin down. The present
volume serves as a great basis for future work in this direction.
May 2012
Christos Faloutsos

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