Similarity-Based Clustering Recent Developments and Biomedical Appli
This book is the outcome of the Dagstuhl Seminar on "Similarity-Based Clustering" held at Dagstuhl Castle, Germany, in Spring 2007. In three chapters, the three fundamental aspects of a theoretical background, the representation of data and their con
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Subseries of Lecture Notes in Computer Science
5400
Michael Biehl Barbara Hammer Michel Verleysen Thomas Villmann (Eds.)
Similarity-Based Clustering Recent Developments and Biomedical 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 Editors Michael Biehl University Groningen, Mathematics and Computing Science Intelligent Systems Group, P.O. Box 407, 9700 AK Groningen, The Netherlands E-mail: [email protected] Barbara Hammer Clausthal University of Technology, Department of Computer Science 38679 Clausthal-Zellerfeld, Germany E-mail: [email protected] Michel Verleysen Université catholique de Louvain, Machine Learning Group, DICE Place du Levant, 3-B-1348, Louvain-la-Neuve, Belgium E-mail: [email protected] Thomas Villmann University of Applied Sciences Mittweida Dep. of Mathematics/Physics/Computer Sciences Technikumplatz 17, 09648 Mittweida, Germany E-mail: [email protected]
Library of Congress Control Number: Applied for CR Subject Classification (1998): H.3.3, I.5.3, I.5.4, J.3, F.1.1, I.2.6 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-642-01804-1 Springer Berlin Heidelberg New York 978-3-642-01804-6 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, 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 German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. springer.com © Springer-Verlag Berlin Heidelberg 2009 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12654949 06/3180 543210
A physicist once came to Dagstuhl and thought: ‘The castle is quite cool!’ ‘So, clearly’, he stated, ‘we should replicate it! And learn from one instance the right rule.’
Preface
Similarity-based learning methods have a great potential as an intuitive and flexible toolbox for mining, visualization, and inspection of large data sets. They combine simple and human-understandable principles, such as distance-based classification, prototypes, or Hebbian learning, with a large variety of different, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composit
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