Volume 7 Number 1 (Jan. 2012)
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JSW 2012 Vol.7(1): 95-101 ISSN: 1796-217X
doi: 10.4304/jsw.7.1.95-101

A New Text Clustering Algorithm Based on Improved K_means

Li Xinwu
Electronic Business department, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

Abstract—Text clustering is one of the difficult and hot research fields in the internet search engine research. A new text clustering algorithm is presented based on Kmeans and Self-Organizing Model (SOM). Firstly, texts are preprocessed to satisfy succeed process requirement. Secondly, the paper improves selection of initial cluster centers and cluster seed selection methods of K-means to improve the deficiency of K-means algorithm that the Kmeans algorithm is very sensitive to the initial cluster center and the isolated point text. Thirdly the advantages of kmeans and SOM are combined to a new model to cluster text in the paper. Finally the experimental results indicate that the improved algorithm has a higher accuracy compared with the original algorithm, and has a better stability.

Index Terms—Text Clustering, K-means clustering, Self- Organizing Model

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Cite:Li Xinwu, "A New Text Clustering Algorithm Based on Improved K_means," Journal of Software vol. 7, no.1, pp. 95-101, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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