Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Automatic Text Categorization and Its Application to Text Retrieval
IEEE Transactions on Knowledge and Data Engineering
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Interpretations of Association Rules by Granular Computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Multi-Tier Granule Mining for Representations of Multidimensional Association Rules
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Deploying Approaches for Pattern Refinement in Text Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Concept-Based, Personalized Web Information Gathering: A Survey
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
A knowledge-based model using ontologies for personalized web information gathering
Web Intelligence and Agent Systems
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Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.