From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Probabilistic Rough Induction: The GDT-RS Methodology and Algorithms
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Rough Set Analysis of Preference-Ordered Data
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Gastric Cancer Data Mining with Ordered Information
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness, Peculiarity, and Multi-database Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Active mining project: overview
AM'03 Proceedings of the Second international conference on Active Mining
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When therapy using IFN (interferon) medication for chronic hepatitis patients, various conceptual knowledge/rules will benefit for giving a treatment. The paper describes our work on cooperatively using various data mining agents including the GDT-RS inductive learning system for discovering decision rules, the LOI (learning with ordered information) for discovering ordering rules and important features, as well as the POM (peculiarity oriented mining) for finding peculiarity data/rules, in a spiral discovery process with multi-phase such as pre-processing, rule mining, and post-processing, for multi-aspect analysis of the hepatitis data and meta learning. Our methodology and experimental results show that the perspective of medical doctors will be changed from a single type of experimental data analysis towards a holistic view, by using our multi-aspect mining approach.