Similarity-Based Clustering

Recent Developments and Biomedical Applications

  • Michael Biehl
  • Barbara Hammer
  • Michel Verleysen
  • Thomas Villmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5400)

Table of contents

  1. Front Matter
  2. Chapter I: Dynamics of Similarity-Based Clustering

    1. Michael Biehl, Nestor Caticha, Peter Riegler
      Pages 1-22
    2. Thomas Villmann, Barbara Hammer, Michael Biehl
      Pages 23-34
  3. Chapter II: Information Representation

    1. Michel Verleysen, Fabrice Rossi, Damien François
      Pages 52-69
    2. Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert
      Pages 70-91
    3. Barbara Hammer, Alexander Hasenfuss, Fabrice Rossi
      Pages 92-117
    4. Nikolaos Gianniotis, Peter Tiňo
      Pages 118-137
  4. Chapter III: Particular Challenges in Applications

  5. Back Matter

About this book

Introduction

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 connection to algorithms, and particular challenging applications are considered. Topics discussed concern a theoretical investigation and foundation of prototype based learning algorithms, the development and extension of models to directions such as general data structures and the application for the domain of medicine and biology.

Similarity based methods find widespread applications in diverse application domains, including biomedical problems, but also in remote sensing, geoscience or other technical domains. The presentations give a good overview about important research results in similarity-based learning, whereby the character of overview articles with references to correlated research articles makes the contributions particularly suited for a first reading concerning these topics.

Keywords

Extension algorithms bioinformatics biology classification feature selection learning machine learning modeling neural gas optimization probabilistic modeling statistical mechanics vector quantization visualization

Editors and affiliations

  • Michael Biehl
    • 1
  • Barbara Hammer
    • 2
  • Michel Verleysen
    • 3
  • Thomas Villmann
    • 4
  1. 1.Mathematics and Computing Science, Intelligent Systems GroupUniversity GroningenGroningenNetherlands
  2. 2.Department of Computer ScienceClausthal University of TechnologyClausthal-ZellerfeldGermany
  3. 3.Machine Learning Group, DICE, Place du LevantUniversité catholique de Louvain,Louvain-la-NeuveBelgium
  4. 4.Dep. of Mathematics/Physics/Computer SciencesUniversity of Applied Sciences MittweidaMittweidaGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-01805-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-01804-6
  • Online ISBN 978-3-642-01805-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book