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Research on Text Clustering Algorithm Based on K_means and SOM

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1 Author(s)
Li Xinwu ; Electron. Bus. Dept., Jiangxi Univ. of Finance & Econ., Nanchang

Text clustering is one of the difficult and hot research fields in the Internet search engine research. Combination the advantages of k-means clustering and self-organizing model (SOM) techniques, a new text clustering algorithm is presented. Firstly, texts are preprocessed to satisfy succeed process. Then, the paper analyzes common k-means clustering algorithm and SOM algorithm and combines them to overcome efficiency of low stability of k-means algorithm which is very sensitive to the initial cluster center and the isolated point text. The experimental results indicate that the improved algorithm has a higher accuracy and has a better stability, compared with the original algorithm.

Published in:

Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on

Date of Conference:

21-22 Dec. 2008