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Graph-Based Clustering and Data Visualization Algorithms

  • Ágnes Vathy-Fogarassy
  • János Abonyi

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Ágnes Vathy-Fogarassy, János Abonyi
    Pages 1-16
  3. Ágnes Vathy-Fogarassy, János Abonyi
    Pages 17-41
  4. Ágnes Vathy-Fogarassy, János Abonyi
    Pages 43-91
  5. Back Matter
    Pages 93-110

About this book

Introduction

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

Authors and affiliations

  • Ágnes Vathy-Fogarassy
    • 1
  • János Abonyi
    • 2
  1. 1.Computer Science and Systems TechnologyUniversity of PannoniaVeszprémHungary
  2. 2.Dept. of Process EngineeringUniversity of PannoniaVeszprémHungary

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-5158-6
  • Copyright Information János Abonyi 2013
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-5157-9
  • Online ISBN 978-1-4471-5158-6
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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