Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems
Fuzzy Sets and Systems
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Peculiarity Oriented Multi-database Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Machine Learning of Credible Classifications
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
The entropy of relations and a new approach for decision tree learning
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
| Hi-index | 0.00 |
The appraisement of rules and rule sets is very important in data mining. The information content of rules is discussed in this paper and is categorized into inner mutual information and outer impartation information. We put forward the viewpoint that the outer impartation information content of rules and rule sets can be represented by relations from input universe to output universe. Then, the interaction of rules in a rule set can be represented by the union and intersection of binary relations expediently. Based on the entropy of relations, the outer impartation information content of rules and rule sets are well measured. Compared with the methods which appraise rule sets by their overall performance (accuracy, error rate) on the given test data sets, the outer impartation information content of rule sets is more objective and convenient because of the absence of test data sets.