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Graph-Based Clustering and Data Visualization Algorithms

  • Book
  • © 2013

Overview

Authors:
  1. Ágnes Vathy-Fogarassy

    1. Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary

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  2. János Abonyi

    1. Dept. of Process Engineering, University of Pannonia, Veszprém, Hungary

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Examines vector quantization methods, and discusses the advantages and disadvantages of minimal spanning tree-based clustering
  • Presents a novel similarity measure to improve the classical Jarvis-Patrick clustering algorithm
  • Reviews distance-, neighborhood- and topology-based dimensionality reduction methods, and introduces new graph-based visualization algorithms

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

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Table of contents (3 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Vector Quantisation and Topology Based Graph Representation

    • Ágnes Vathy-Fogarassy, János Abonyi
    Pages 1-16

  3. Graph-Based Clustering Algorithms

    • Ágnes Vathy-Fogarassy, János Abonyi
    Pages 17-41

  4. Graph-Based Visualisation of High Dimensional Data

    • Ágnes Vathy-Fogarassy, János Abonyi
    Pages 43-91

  5. Back Matter

    Pages 93-110
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About this book

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

  • Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary

    Ágnes Vathy-Fogarassy

  • Dept. of Process Engineering, University of Pannonia, Veszprém, Hungary

    János Abonyi

Bibliographic Information

  • Book Title: Graph-Based Clustering and Data Visualization Algorithms

  • Authors: Ágnes Vathy-Fogarassy, János Abonyi

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-1-4471-5158-6

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: János Abonyi 2013

  • Softcover ISBN: 978-1-4471-5157-9Published: 05 June 2013

  • eBook ISBN: 978-1-4471-5158-6Published: 24 May 2013

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: XIII, 110

  • Number of Illustrations: 62 b/w illustrations

  • Topics: Data Mining and Knowledge Discovery, Visualization

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