Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Surprising Exception Rules Based on Intensity of Implication
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
Reducing redundancy in characteristic rule discovery by using integer programming techniques
Intelligent Data Analysis
Random worlds and maximum entropy
Journal of Artificial Intelligence Research
A Data Mining Approach to New Library Book Recommendations
ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
Mining dependence rules by finding largest itemset support quota
Proceedings of the 2004 ACM symposium on Applied computing
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Itemset frequency satisfiability: Complexity and axiomatization
Theoretical Computer Science
Visual Exploration of Frequent Itemsets and Association Rules
Visual Data Mining
Maximum entropy based significance of itemsets
Knowledge and Information Systems
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Mining non-redundant information-theoretic dependencies between itemsets
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Finding trees from unordered 0–1 data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Data mining algorithms produce huge sets of rules, practically impossible to analyze manually. It is thus important to develop methods for removing redundant rules from those sets. We present a solution to the problem using the Maximum Entropy approach. The problem of efficiency of Maximum Entropy computations is addressed by using closed form solutions for the most frequent cases. Analytical and experimental evaluation of the proposed technique indicates that it efficiently produces small sets of interesting association rules.