Volume 8 Number 12 ( Dec. 2013)
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JSW 2013 Vol.8(12): 3246-3252 ISSN: 1796-217X
doi: 10.4304/jsw.8.12.3246-3252

Research of Feature Selection for Text Clustering Based on Cloud Model

Junmin Zhao1, Kai Zhang1, Jian Wan2
1Henan University of Urban Construction/Institute of Computer Science and Engineering, Pingdingshan, China
2ZhengZhou ShiYi Technology Co. Ltd, Zhengzhou, China

Abstract—Text clustering belongs to the unsupervised machine learning, the discriminability of class attributes cannot be measured in clustering. And the traditional text feature selection methods cannot effectively solve the high-dimensional problem. To overcome the weakness in existing feature selection, this paper proposes a new method which introduces the cloud model theory into feature selection, constructs the clouds filter for clustering documents. The distribution of document words is constructed in a microcosmic level. By employing the cloud model digital characteristics we can better compute the separability between feature words. Experimental results with K-means algorithm show that our method can remarkably improve the accuracy of text clustering.

Index Terms—Feature selection, cloud model, TF-IDF, K-means algorithm.


Cite: Junmin Zhao, Kai Zhang, Jian Wan, "Research of Feature Selection for Text Clustering Based on Cloud Model," Journal of Software vol. 8, no. 12, pp. 3246-3252, 2013.

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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