The Comparison of SOM and K-means for Text Clustering

@article{Chen2010TheCO,
  title={The Comparison of SOM and K-means for Text Clustering},
  author={Yiheng Chen and Bing Qin and Ting Liu and Yuanchao Liu and Sheng Li},
  journal={Computer and Information Science},
  year={2010},
  volume={3},
  pages={268-274}
}
SOM and k-means are two classical methods for text clustering. In this paper some experiments have been done to compare their performances. The sample data used is 420 articles which come from different topics. K-means method is simple and easy to implement; the structure of SOM is relatively complex, but the clustering results are more visual and easy to comprehend. The comparison results also show that k-means is sensitive to initiative distribution, whereas the overall clustering performance… CONTINUE READING

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