Vector quantisation and topology based graph representation

Ágnes Vathy-Fogarassy, J. Abonyi

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
Number of pages16
Publication statusPublished - Jan 1 2013

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776



  • Cluster centre
  • Delaunay triangulation
  • Minimal span tree
  • Vector quantisation
  • Voronoi diagram

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Vathy-Fogarassy, Á., & Abonyi, J. (2013). Vector quantisation and topology based graph representation. In SpringerBriefs in Computer Science (9781447151579 ed., pp. 1-16). (SpringerBriefs in Computer Science; No. 9781447151579). Springer.