Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
ECML '93 Proceedings of the European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
eMailSift: mining-based approaches to email classification
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Adapting association patterns for text categorization: weaknesses and enhancements
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
On the strength of hyperclique patterns for text categorization
Information Sciences: an International Journal
Word co-occurrence features for text classification
Information Systems
Sentential association based text classification systems
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Feature selection, rule extraction, and score model: making ATC competitive with SVM
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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In this paper, we present a novel association-based method called SAT-MOD for text classification. SAT-MOD views a sentence rather than a document as a transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. The effectiveness of SAT-MOD has been demonstrated comparable to well-known alternatives such as LinearSVM and much better than current document-level words association based methods on the Reuters corpus.