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Similarity-Based Clustering

Recent Developments and Biomedical Applications

  • Book
  • © 2009

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Editors:
  1. Michael Biehl

    1. Mathematics and Computing Science, Intelligent Systems Group, University Groningen, Groningen, Netherlands

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  2. Barbara Hammer

    1. Department of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

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  3. Michel Verleysen

    1. Machine Learning Group, DICE, Place du Levant, Université catholique de Louvain,, Louvain-la-Neuve, Belgium

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  4. Thomas Villmann

    1. Dep. of Mathematics/Physics/Computer Sciences, University of Applied Sciences Mittweida, Mittweida, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5400)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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About this book

Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, 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 composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

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Table of contents (10 chapters)

  1. Front Matter

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  2. Chapter I: Dynamics of Similarity-Based Clustering

    1. Statistical Mechanics of On-line Learning

      • Michael Biehl, Nestor Caticha, Peter Riegler
      Pages 1-22

    2. Some Theoretical Aspects of the Neural Gas Vector Quantizer

      • Thomas Villmann, Barbara Hammer, Michael Biehl
      Pages 23-34

    3. Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings

      • Colin Fyfe, Wesam Barbakh
      Pages 35-51

  3. Chapter II: Information Representation

    1. Advances in Feature Selection with Mutual Information

      • Michel Verleysen, Fabrice Rossi, Damien François
      Pages 52-69

    2. Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data

      • Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert
      Pages 70-91

    3. Median Topographic Maps for Biomedical Data Sets

      • Barbara Hammer, Alexander Hasenfuss, Fabrice Rossi
      Pages 92-117

    4. Visualization of Structured Data via Generative Probabilistic Modeling

      • Nikolaos Gianniotis, Peter Tiňo
      Pages 118-137

  4. Chapter III: Particular Challenges in Applications

    1. Learning Highly Structured Manifolds: Harnessing the Power of SOMs

      • Erzsébet Merényi, Kadim Tasdemir, Lili Zhang
      Pages 138-168

    2. Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images

      • Lidia Sánchez, Nicolai Petkov
      Pages 169-184

    3. HIV-1 Drug Resistance Prediction and Therapy Optimization: A Case Study for the Application of Classification and Clustering Methods

      • Michal Rosen-Zvi, Ehud Aharoni, Joachim Selbig
      Pages 185-201

  5. Back Matter

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Editors and Affiliations

  • Mathematics and Computing Science, Intelligent Systems Group, University Groningen, Groningen, Netherlands

    Michael Biehl

  • Department of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Barbara Hammer

  • Machine Learning Group, DICE, Place du Levant, Université catholique de Louvain,, Louvain-la-Neuve, Belgium

    Michel Verleysen

  • Dep. of Mathematics/Physics/Computer Sciences, University of Applied Sciences Mittweida, Mittweida, Germany

    Thomas Villmann

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