Hard and Fuzzy c-Medoids for Asymmetric Networks
- 10.2991/ifsa-eusflat-15.2015.63How to use a DOI?
- fuzzy c-medoids, asymmetric dissimilarity, SNS.
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.
- © 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/).
Cite this article
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 -