Scalable association-based text classification
Proceedings of the ninth international conference on Information and knowledge management
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
SAT-MOD: moderate itemset fittest for text classification
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
2-PS based associative text classification
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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We recently proposed a novel sentential association based approach SAT-MOD for text classification, which views a sentence rather than a document as an association transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. Based on SAT-MOD, we have developed a prototype system called SAT-Class. In this demo, we demonstrate the effectiveness of our text classification system, and also the readability and refinability of acquired classification rules.