Fuzzy Graph Clustering based on Non-Euclidean Relational Fuzzy c-Means

DOI

10.2991/ifsa-eusflat-15.2015.16How to use a DOI?

Keywords

Graph clustering, relational clustering, social networks

Abstract

Graph clustering is a very popular research field with numerous practical applications. Here we focus on finding fuzzy clusters of nodes in unweighted, undirected, and irreflexive graphs. We introduce three new algorithms for fuzzy graph clustering (Newman–Girvan NERFCM, Small World NERFCM, Signal NERFCM). Each of these three new algorithms uses a popular algorithm for crisp graph clustering and combines it with non–Euclidean relational fuzzy c–means clustering (NERFCM). Experiments with artificial and real world data indicate that all three proposed algorithms perform quite well for compact clusters. For less compact clusters, Newman–Girvan NERFCM and Signal NERFCM also perform well. Newman–Girvan NERFCM is more robust to cluster overlaps, and Signal NERFCM yields very smooth membership transitions.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Thomas A. Runkler
AU  - Vikram Ravindra
PY  - 2015/06
DA  - 2015/06
TI  - Fuzzy Graph Clustering based on Non-Euclidean Relational Fuzzy c-Means
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 91
EP  - 97
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.16
DO  - 10.2991/ifsa-eusflat-15.2015.16
ID  - Runkler2015/06
ER  -