Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Timeline Analysis of Web News Events
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
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The current research on association rule based text classification neglected several key problems. First, weights of elements in profile vectors may have much impact on generating classification rules. Second, traditional association rule lacks semantics. Increasing semantic of association rule may help to improve the classification accuracy. Focusing on the above problems, we propose a new classification approach. This approach include: (1) Mining frequent item-sets on item-weighted transactions; (2) Generating enhanced association rule that has richer semantics than traditional association rule. Experiments show that new approach outperforms CMAR, S-EM and NB algorithms on classification accuracy.