Hierarchical Intuitionistic Fuzzy Possibilistic C Means Kernel Clustering Algorithm for Distributed Networks

DOI

10.2991/ifsa-eusflat-15.2015.150How to use a DOI?

Keywords

Hierarchical clustering, intuitionistic fuzzy set, peer-to-peer network, large datasets, kernel cluster-ing, fuzzy possibilistic c-means.

Abstract

Advances in distributed networking have resulted in an explosion in size of modern datasets while storage and processing power continue to lag behind. This requires the need for algorithms that are efficient in terms of number of measurements and running time. To combat challenges associated with large datasets in distributed networks we propose hierarchical intuitionistic fuzzy possibilistic c-means kernel clustering algorithm. The algorithm executes hierarchically by performing clus-tering at each peer. The intuitionistic fuzzy degree and tipicality membership functions and weight-attribute-entropy factor improves clustering performance. The experiments on artificial and real datasets establish the efficiency and effectiveness of the algorithm.

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  - Arindam Chaudhuri
AU  - Soumya K. Ghosh
PY  - 2015/06
DA  - 2015/06
TI  - Hierarchical Intuitionistic Fuzzy Possibilistic C Means Kernel Clustering Algorithm for Distributed 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  - 1060
EP  - 1067
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.150
DO  - 10.2991/ifsa-eusflat-15.2015.150
ID  - Chaudhuri2015/06
ER  -