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
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Generating non-redundant association rules
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
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
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
Expected Error Analysis for Model Selection
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Mining the Smallest Association Rule Set for Predictions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Computable Shell Decomposition Bounds
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
Reducing redundancy in characteristic rule discovery by using integer programming techniques
Intelligent Data Analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Significant Patterns
Machine Learning
Artificial Intelligence in Medicine
User Modeling and User-Adapted Interaction
Models for association rules based on clustering and correlation
Intelligent Data Analysis
Evaluating Web Based Instructional Models Using Association Rule Mining
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Unsupervised ontology acquisition from plain texts: the OntoGain system
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Association rules to identify receptor and ligand structures through named entities recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Discovering access-control misconfigurations: new approaches and evaluation methodologies
Proceedings of the second ACM conference on Data and Application Security and Privacy
Editorial: Efficient discovery of similarity constraints for matching dependencies
Data & Knowledge Engineering
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering frequent pattern pairs
Intelligent Data Analysis
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When evaluating association rules, rules that differ in both support and confidence have to be 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.