Fast discovery of association rules
Advances in knowledge discovery and data mining
Pruning and summarizing the discovered associations
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
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
The Representative Basis for Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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
Fast Discovery of Representative Association Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Finding association rules that trade support optimally against confidence
Intelligent Data Analysis
Discovering Significant Patterns
Machine Learning
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Two measures of objective novelty in association rule mining
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
Border algorithms for computing hasse diagrams of arbitrary lattices
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
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Some existing notions of redundancy among association rules allow for a logical-style characterization and lead to irredundant bases of absolutely minimum size. We push the intuition of redundancy further to find an intuitive notion of novelty of an association rule, with respect to other rules. Namely, an irredundant rule is so because its confidence is higher than what the rest of the rules would suggest; then, one can ask: how much higher? We propose to measure such a sort of novelty through the confidence boost of a rule. Acting as a complement to confidence and support, the confidence boost helps to obtain small and crisp sets of mined association rules and solves the well-known problem that, in certain cases, rules of negative correlation may pass the confidence bound. We analyze the properties of two versions of the notion of confidence boost, one of them a natural generalization of the other. We develop algorithms to filter rules according to their confidence boost, compare the concept to some similar notions in the literature, and describe the results of some experimentation employing the new notions on standard benchmark datasets. We describe an open source association mining tool that embodies one of our variants of confidence boost in such a way that the data mining process does not require the user to select any value for any parameter.