Classification as a Tool for Research

Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation e.V., Dresden, March 13-18, 2009

Editors:

ISBN: 978-3-642-10744-3 (Print) 978-3-642-10745-0 (Online)

Table of contents (90 chapters)

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  1. Front Matter

    Pages i-xxxvi

  2. (Semi-) Plenary Presentations

    1. Front Matter

      Pages 1-2

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      Pages 3-14

      Hierarchical Clustering with Performance Guarantees

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      Pages 15-24

      Alignment Free String Distances for Phylogeny

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      Pages 25-36

      Data Quality Dependent Decision Making in Pattern Classification

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      Pages 37-48

      Clustering Proteins and Reconstructing Evolutionary Events

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      Pages 49-60

      Microarray Dimension Reduction Based on Maximizing Mantel Correlation Coefficients Using a Genetic Algorithm Search Strategy

  3. Classification and Data Analysis

    1. Front Matter

      Pages 61-62

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      Pages 63-70

      Multiparameter Hierarchical Clustering Methods

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      Pages 71-79

      Unsupervised Sparsification of Similarity Graphs

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      Pages 81-89

      Simultaneous Clustering and Dimensionality Reduction Using Variational Bayesian Mixture Model

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      Pages 91-99

      A Partitioning Method for the Clustering of Categorical Variables

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      Pages 101-108

      Treed Gaussian Process Models for Classification

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      Pages 109-116

      Ridgeline Plot and Clusterwise Stability as Tools for Merging Gaussian Mixture Components

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      Pages 117-125

      Clustering with Confidence: A Low-Dimensional Binning Approach

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      Pages 127-135

      Local Classification of Discrete Variables by Latent Class Models

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      Pages 137-145

      A Comparative Study on Discrete Discriminant Analysis through a Hierarchical Coupling Approach

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      Pages 147-155

      A Comparative Study of Several Parametric and Semiparametric Approaches for Time Series Classification

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      Pages 157-165

      Finite Dimensional Representation of Functional Data with Applications

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      Pages 167-175

      Clustering Spatio-Functional Data: A Model Based Approach

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      Pages 177-184

      Use of Mixture Models in Multiple Hypothesis Testing with Applications in Bioinformatics

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      Pages 185-192

      Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures

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      Book Chapter

      Pages 193-200

      An Application of One-mode Three-way Overlapping Cluster Analysis

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