Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A sequential sampling algorithm for a general class of utility criteria
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Nonparametric Regularization of Decision Trees
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Computable Shell Decomposition Bounds
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Using Data Mining Techniques to Discover Bias Patterns in Missing Data
Journal of Data and Information Quality (JDIQ)
Intelligent phishing detection system for e-banking using fuzzy data mining
Expert Systems with Applications: An International Journal
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach
Journal of Medical Systems
Classification of type-2 diabetic patients by using Apriori and predictive Apriori
International Journal of Computational Vision and Robotics
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Voice of the customer: Customer satisfaction ratio based analysis
Expert Systems with Applications: An International Journal
A Theory of Evidence-based method for assessing frequent patterns
Expert Systems with Applications: An International Journal
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When evaluating association rules, rules that differ in both support and confidence have to compared; a larger support has to be traded against a higher confidence. The solution which we propose for this problem is to maximize the expected accuracy that the association rule will have for future data. In a Bayesian framework, we determine the contributions of confidence and support to the expected accuracy on future data. We present a fast algorithm that finds the n best rules which maximize the resulting criterion. The algorithm dynamically prunes redundant rules and parts of the hypothesis space that cannot contain better solutions than the best ones found so far. We evaluate the performance of the algorithm (relative to the Apriori algorithm) on realistic knowledge discovery problems.