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Similarity matrix-based K-means algorithm for text clustering

Similarity matrix-based K-means algorithm for text clustering
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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 al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved 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. 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 al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved 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.
作者 曹奇敏 郭巧 吴向华
机构地区 School of Automation
出处 《Journal of Beijing Institute of Technology》 EI CAS 2015年第4期566-572,共7页 北京理工大学学报（英文版）
关键词 text clustering K-means algorithm similarity matrix F-MEASURE text clustering K-means algorithm similarity matrix F-measure
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
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Journal of Beijing Institute of Technology
2015年 第4期

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