Fuzzy c-medoid graph clustering

András Király, Ágnes Vathy-Fogarassy, J. 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages738-748
Number of pages11
Volume8468 LNAI
EditionPART 2
ISBN (Print)9783319071756
DOIs
Publication statusPublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Graph Clustering
Cluster Validity Index
Dijkstra Algorithm
Geodesic Distance
Centrality
Graph in graph theory
Clustering

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Király, A., Vathy-Fogarassy, Á., & Abonyi, J. (2014). Fuzzy c-medoid graph clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 8468 LNAI, 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

Fuzzy c-medoid graph clustering. / Király, András; Vathy-Fogarassy, Ágnes; Abonyi, J.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8468 LNAI PART 2. ed. Springer Verlag, 2014. p. 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).

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

Király, A, Vathy-Fogarassy, Á & Abonyi, J 2014, Fuzzy c-medoid graph clustering. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 8468 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8468 LNAI, Springer Verlag, pp. 738-748, 13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014, Zakopane, Poland, 6/1/14. https://doi.org/10.1007/978-3-319-07176-3_64
Király A, Vathy-Fogarassy Á, Abonyi J. Fuzzy c-medoid graph clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 8468 LNAI. Springer Verlag. 2014. p. 738-748. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-319-07176-3_64
Király, András ; Vathy-Fogarassy, Ágnes ; Abonyi, J. / Fuzzy c-medoid graph clustering. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8468 LNAI PART 2. ed. Springer Verlag, 2014. pp. 738-748 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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