Research and Improvement on K-Means Clustering Algorithm
- 10.2991/iccia.2012.280How to use a DOI?
- differential evolution algorithm, K-means cluster algorithm,Cluster analysis
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.
- © 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/).
Cite this article
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 -