A new hybrid clustering algorithm based on K-means and ant colony algorithm

DOI

10.2991/iccsee.2013.430How to use a DOI?

Keywords

similarity, data mining, clustering

Abstract

K-means algorithm and ant clustering algorithm are all traditional algorithms. The two algorithms can complement each other. The combination of two algorithms will improve clustering’s accuracy and speed up algorithm’ convergence. Tests prove hybrid clustering algorithm is more effective than each above-mentioned algorithm. Especially, the new algorithm has good results in image segmentation.

Copyright

© 2013, 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  - Jue Lu
AU  - Rongqiang Hu
PY  - 2013/03
DA  - 2013/03
TI  - A new hybrid clustering algorithm based on K-means and ant colony algorithm
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1718
EP  - 1721
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.430
DO  - 10.2991/iccsee.2013.430
ID  - Lu2013/03
ER  -