C4.5: programs for machine learning
C4.5: programs for machine learning
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Readings in knowledge acquisition and learning: automating the construction and improvement of expert systems
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
The goal of data mining is to discover useful knowledge (or rules) for the user. Past research has produced many efficient techniques for rule discovery from databases. However, these techniques often generate too many rules, which makes it very difficult for the user to analyze them in order to find those truly interesting/useful rules. In this article, we first discuss some issues involved in assisting the user to analyze the discovered rules. We then review a number of existing techniques that employ the user's preferences and knowledge about the domain to identify those potentially interesting/useful rules for the user.