Hard and Fuzzy c-Medoids for Asymmetric Networks

DOI

10.2991/ifsa-eusflat-15.2015.63How to use a DOI?

Keywords

fuzzy c-medoids, asymmetric dissimilarity, SNS.

Abstract

Medoid clustering frequently gives better results than those of the K-means clustering in the sense that a unique object is the representative element of a cluster. Moreover the method of medoids can be applied to nonmetric cases such as weighted graphs that arise in analyzing SNS(Social Networking Service) networks. A general problem in clustering is that asymmetric measures of similarity or dissimilarity are difficult to handle, while relations are asymmetric, e.g., in SNS user groups. In this pa- per we consider hard and fuzzy c-medoids for asymmetric graphs in which a cluster has two different centers with outgoing directions and incoming directions. This method is applied to a small illustrative example and real data of a Twitter user network.

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  - Yousuke Kaizu
AU  - Sadaaki Miyamoto
AU  - Yasunori Endo
PY  - 2015/06
DA  - 2015/06
TI  - Hard and Fuzzy c-Medoids for Asymmetric Networks
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  - 435
EP  - 440
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.63
DO  - 10.2991/ifsa-eusflat-15.2015.63
ID  - Kaizu2015/06
ER  -