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Vector Quantisation and Topology Based Graph Representation

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Graph-Based Clustering and Data Visualization Algorithms

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

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Abstract

Compact graph based representation of complex data can be used for clustering and visualisation. In this chapter we introduce basic concepts of graph theory and present approaches which may generate graphs from data. Computational complexity of clustering and visualisation algorithms can be reduced replacing original objects with their representative elements (code vectors or fingerprints) by vector quantisation. We introduce widespread vector quantisation methods, the \(k\)-means and the neural gas algorithms. Topology representing networks obtained by the modification of neural gas algorithm create graphs useful for the low-dimensional visualisation of data set. In this chapter the basic algorithm of the topology representing networks and its variants (Dynamic Topology Representing Network and Weighted Incremental Neural Network) are presented in details.

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Authors and Affiliations

  1. Computer Science and Systems Technology, University of Pannonia, Egyetem u.10, Veszprém, 8200, Hungary

    Ágnes Vathy-Fogarassy

  2. Department of Process Engineering, University of Pannonia, Egyetem u. 10, Veszprém, 8200, Hungary

    János Abonyi

Authors
  1. Ágnes Vathy-Fogarassy
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  2. János Abonyi
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Correspondence to Ágnes Vathy-Fogarassy .

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© 2013 János Abonyi

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Vathy-Fogarassy, Á., Abonyi, J. (2013). Vector Quantisation and Topology Based Graph Representation. In: Graph-Based Clustering and Data Visualization Algorithms. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5158-6_1

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  • DOI: https://doi.org/10.1007/978-1-4471-5158-6_1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5157-9

  • Online ISBN: 978-1-4471-5158-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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