节点文献
Similarity matrix-based K-means algorithm for text clustering
【摘要】 K-means algorithm is one of the most widely used algorithms in the clustering analysis.To deal with the problem caused by the random selection of initial center points in the traditional algorithm,this paper proposes an improved K-means algorithm based on the similarity matrix.The improved algorithm can effectively avoid the random selection of initial center points,therefore it can provide effective initial points for clustering process,and reduce the fluctuation of clustering results which are resulted from initial points selections,thus a better clustering quality can be obtained.The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
【Abstract】 K-means algorithm is one of the most widely used algorithms in the clustering analysis.To deal with the problem caused by the random selection of initial center points in the traditional algorithm,this paper proposes an improved K-means algorithm based on the similarity matrix.The improved algorithm can effectively avoid the random selection of initial center points,therefore it can provide effective initial points for clustering process,and reduce the fluctuation of clustering results which are resulted from initial points selections,thus a better clustering quality can be obtained.The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
【Key words】 text clustering; K-means algorithm; similarity matrix; F-measure;
- 【文献出处】 Journal of Beijing Institute of Technology ,北京理工大学学报(英文版) , 编辑部邮箱 ,2015年04期
- 【分类号】TP391.1
- 【被引频次】5
- 【下载频次】99