¹Henan University of Urban Construction/Institute of Computer Science and Engineering, Pingdingshan, China
²ZhengZhou ShiYi Technology Co. Ltd, Zhengzhou, China

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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.

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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.