Fuzzy c-medoid graph clustering

András Király, Ágnes Vathy-Fogarassy, János Abonyi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a modified fuzzy c-medoid algorithm to find central objects in graphs. Initial cluster centres are determined by graph centrality measures. Cluster centres are fine-tuned by minimizing fuzzy-weighted geodesic distances calculated by Dijkstra's algorithm. Cluster validity indices show significant improvement against fuzzy c-medoid clustering.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 13th International Conference, ICAISC 2014, Proceedings
PublisherSpringer Verlag
Pages738-748
Number of pages11
EditionPART 2
ISBN (Print)9783319071756
DOIs
Publication statusPublished - Jan 1 2014
Event13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014 - Zakopane, Poland
Duration: Jun 1 2014Jun 5 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8468 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014
CountryPoland
CityZakopane
Period6/1/146/5/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Király, A., Vathy-Fogarassy, Á., & Abonyi, J. (2014). Fuzzy c-medoid graph clustering. In Artificial Intelligence and Soft Computing - 13th International Conference, ICAISC 2014, Proceedings (PART 2 ed., pp. 738-748). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8468 LNAI, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-319-07176-3_64