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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2015> 24(4): 566-572
CAO Qi-min, GUO Qiao, WU Xiang-hua. Similarity matrix-based K-means algorithm for text clustering[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2015, 24(4): 566-572. doi: 10.15918/j.jbit1004-0579.201524.0421
Citation: CAO Qi-min, GUO Qiao, WU Xiang-hua. Similarity matrix-basedK-means algorithm for text clustering[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2015, 24(4): 566-572.doi:10.15918/j.jbit1004-0579.201524.0421
shu

Similarity matrix-basedK-means algorithm for text clustering

doi:10.15918/j.jbit1004-0579.201524.0421

  • School of Automation, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2014-04-14
    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.
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