Research and Improvement on K-Means Clustering Algorithm

DOI

10.2991/iccia.2012.280How to use a DOI?

Keywords

differential evolution algorithm, K-means cluster algorithm,Cluster analysis

Abstract

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability and more effectively improved clustering quality and convergence speed.

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  - Xuemei Wang
AU  - Jinbo Wang
PY  - 2014/05
DA  - 2014/05
TI  - Research and Improvement on K-Means Clustering Algorithm
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 1138
EP  - 1141
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.280
DO  - 10.2991/iccia.2012.280
ID  - Wang2014/05
ER  -