Selected Contributions in Data Analysis and Classification

ISBN: 978-3-540-73558-8 (Print) 978-3-540-73560-1 (Online)

Table of contents (59 chapters)

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  1. Front Matter

    Pages I-XIII

  2. Analysis of Symbolic Data

    1. Front Matter

      Pages 1-1

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      Chapter

      Pages 3-12

      Dependencies and Variation Components of Symbolic Interval-Valued Data

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      Chapter

      Pages 13-22

      On the Analysis of Symbolic Data

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      Chapter

      Pages 23-33

      Symbolic Analysis to Learn Evolving CyberTraffic

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      Chapter

      Pages 35-44

      A Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Hausdorff Distance

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      Chapter

      Pages 45-53

      An Agglomerative Hierarchical Clustering Algorithm for Improving Symbolic Object Retrieval

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      Chapter

      Pages 55-67

      3WaySym-Scal: Three-Way Symbolic Multidimensional Scaling

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      Chapter

      Pages 69-81

      Clustering and Validation of Interval Data

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      Chapter

      Pages 83-94

      Building Symbolic Objects from Data Streams

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      Chapter

      Pages 95-102

      Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data

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      Chapter

      Pages 103-111

      Symbolic Markov Chains

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      Chapter

      Pages 113-122

      Quality Issues in Symbolic Data Analysis

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      Chapter

      Pages 123-134

      Dynamic Clustering of Histogram Data: Using the Right Metric

  3. Clustering Methods

    1. Front Matter

      Pages 135-135

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      Chapter

      Pages 137-150

      Beyond the Pyramids: Rigid Clustering Systems

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      Chapter

      Pages 151-159

      Indirect Blockmodeling of 3-Way Networks

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      Chapter

      Pages 161-172

      Clustering Methods: A History of k-Means Algorithms

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      Chapter

      Pages 173-182

      Overlapping Clustering in a Graph Using k-Means and Application to Protein Interactions Networks

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      Chapter

      Pages 183-191

      Species Clustering via Classical and Interval Data Representation

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      Chapter

      Pages 193-201

      Looking for High Density Zones in a Graph

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      Chapter

      Pages 203-212

      Block Bernoulli Parsimonious Clustering Models

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      Chapter

      Pages 213-223

      Cluster Analysis Based on Posets

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