C4.5: programs for machine learning
C4.5: programs for machine learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
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
A General Measure of Rule Interestingness
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Machine Learning of Credible Classifications
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Efficient Search of Reliable Exceptions
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Constructing Inductive Applications by Meta-Learning with Method Repositories
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Data mining from clinical data using interactive evolutionary computation
Advances in evolutionary computing
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
The outer impartation information content of rules and rule sets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Active mining project: overview
AM'03 Proceedings of the Second international conference on Active Mining
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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This research experimentally investigates the performance of conventional rule interestingness measures and discusses their usefulness for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected sixteen kinds of interestingness measures for the rules discovered in a dataset on hepatitis. χ2 measure, recall, and accuracy demonstrated the highest performance, and specificity and prevalence did the lowest. The interestingness measures showed a complementary relationship for each other. These results indicated that some interestingness measures have the possibility to predict really interesting rules at a certain level and that the combinational use of interestingness measures will be useful. We then discussed how to combinationally utilize interestingness measures and proposed a post-processing user interface utilizing them, which supports KDD through human-system interaction.