Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SAT-MOD: moderate itemset fittest for text classification
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
OCFS: optimal orthogonal centroid feature selection for text categorization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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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: First, what kind of association patterns are the best candidate for pattern-based text categorization? Second, what is the most desirable way to use patterns for text categorization? In this paper, we focus on answering the above two questions. 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. The experimental results show that our method provides better performance than state-of-the-art methods in terms of both computational performance and classification accuracy.