Clustering Algorithm Based on Artificial Bee Colony Optimization

DOI

10.2991/asei-15.2015.28How to use a DOI?

Keywords

k-medoids clustering; granular computing; artificial bee colony; selection probability.

Abstract

After analyzing the disadvantages of sensitivity to the initial selection of the center, low clustering accuracy and the poor global search ability of k-medoids clustering algorithm, a clustering algorithm based on improved artificial bee colony (ABC) is proposed. By improving the initialization of bee colony, adjusting the search step dynamically with iteration increasing , and then introducing the selection probability based on sorting instead of depending on fitness directly, the ABC algorithm will quickly converge to global optimal. This paper will further optimize k-medoids to improve the performance of the clustering algorithm. The experimental results show that this algorithm can reduce the sensitive degree of the initial center selection and the noise, has high accuracy and strong stability.

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  - Dandan Zhang
AU  - Ke Luo
PY  - 2015/05
DA  - 2015/05
TI  - Clustering Algorithm Based on Artificial Bee Colony Optimization
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 126
EP  - 131
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.28
DO  - 10.2991/asei-15.2015.28
ID  - Zhang2015/05
ER  -