Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Inverted files versus signature files for text indexing
ACM Transactions on Database Systems (TODS)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Adapting associative classification to text categorization
Proceedings of the 2007 ACM symposium on Document engineering
Linear pattern matching algorithms
SWAT '73 Proceedings of the 14th Annual Symposium on Switching and Automata Theory (swat 1973)
Text Categorization Based on Boosting Association Rules
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
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Associative classification methods have been recently applied to various categorization tasks due to its simplicity and high accuracy. To improve the coverage for test documents and to raise classification accuracy, some associative classifiers generate a huge number of association rules during the mining step. We present two algorithms to increase the computational efficiency of associative classification: one to store rules very efficiently, and the other to increase the speed of rule matching, using all of the generated rules. Empirical results using three large-scale text collections demonstrate that the proposed algorithms increase the feasibility of applying associative classification to large-scale problems.