Graph-based clustering algorithms

Ágnes Vathy-Fogarassy, J. Abonyi

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

The way how graph-based clustering algorithms utilize graphs for partitioning data is very various. In this chapter, two approaches are presented. The first hierarchical clustering algorithm combines minimal spanning trees and Gath-Geva fuzzy clustering. The second algorithm utilizes a neighborhood-based fuzzy similarity measure to improve k-nearest neighbor graph based Jarvis-Patrick clustering.

Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages17-41
Number of pages25
Edition9781447151579
DOIs
Publication statusPublished - Jan 1 2013

Publication series

NameSpringerBriefs in Computer Science
Number9781447151579
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

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Keywords

  • Cluster algorithm
  • Cluster rate
  • Fuzzy cluster
  • Minimal span tree
  • Single linkage

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Vathy-Fogarassy, Á., & Abonyi, J. (2013). Graph-based clustering algorithms. In SpringerBriefs in Computer Science (9781447151579 ed., pp. 17-41). (SpringerBriefs in Computer Science; No. 9781447151579). Springer. https://doi.org/10.1007/978-1-4471-5158-6_2