On the strength of hyperclique patterns for text categorization

Authors:
Tieyun Qian;Hui Xiong;Yuanzhen Wang;Enhong Chen
Affiliations:
Department of Computer Science, Wuhan University, 16 Luojiashan Road, Wuhan, Heibei 430072, China;Management Science and Information Systems Department, Rutgers University, USA;School of Computer Science, Huazhong University of Science and Technology, China;Department of Computer Science, University of Science and Technology of China, China
Venue:
Information Sciences: an International Journal
Year:
2007

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Abstract

The use of association patterns for text categorization has attracted great interest and a variety of useful methods have been developed. However, the key characteristics of pattern-based text categorization remain unclear. Indeed, there are still no concrete answers for the following two questions: Firstly, what kind of association pattern is the best candidate for pattern-based text categorization? Secondly, what is the most desirable way to use patterns for text categorization? In this paper, we focus on answering the above two questions. More specifically, we show that hyperclique patterns are more desirable than frequent patterns for text categorization. Along this line, we develop an algorithm for text categorization using hyperclique patterns. As demonstrated by our experimental results on various real-world text documents, our method provides much better computational performance than state-of-the-art methods while retaining classification accuracy.