Text classification in a hierarchical mixture model for small training sets
Proceedings of the tenth international conference on Information and knowledge management
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automatically learning document taxonomies for hierarchical classification
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Automatic computation of semantic proximity using taxonomic knowledge
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Improving classification based off-topic search detection via category relationships
Proceedings of the 2009 ACM symposium on Applied Computing
Relation Discovery from Thai News Articles Using Association Rule Mining
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Mining temporal relationships among categories
Proceedings of the 2010 ACM Symposium on Applied Computing
Detecting relationships among categories using text classification
Journal of the American Society for Information Science and Technology
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
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Knowledge of relationships among categories is of the interest in different domains such as text classification, content analysis, and text mining. We propose and evaluate approaches to effectively identify relationships among document categories. Our proposed novel method capitalizes on the misclassification results of a text classifier to identify potential relationships among categories. We demonstrate that our system detects such relationships, even those relationships that assessors failed to identify in manual evaluation. Furthermore, we favorably compare the effectiveness of our methods with the state of art method and demonstrate a significant improvement in precision (34%) and recall (5%).