Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Horn approximations of empirical data
Artificial Intelligence
Artificial Intelligence
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
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
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
The Representative Basis for Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Improving the Discovery of Association Rules with 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
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Formal logics of discovery and hypothesis formation by machine
Theoretical Computer Science
Mining Association Algorithm with Threshold based on ROC Analysis
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On support thresholds in associative classification
Proceedings of the 2004 ACM symposium on Applied computing
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
ACM Computing Surveys (CSUR)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Association Rule Mining with Dynamic Adaptive Support Thresholds for Associative Classification
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining formal concepts with a bounded number of exceptions from transactional data
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining mutually dependent patterns for system management
IEEE Journal on Selected Areas in Communications
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Association rule mining is well-known to depend heavily on a support threshold parameter, and on one or more thresholds for intensity of implication; among these measures, confidence is most often used and, sometimes, related alternatives such as lift, leverage, improvement, or all-confidence are employed, either separately or jointly with confidence. We remain within the support-and-confidence framework in an attempt at studying complementary notions, which have the goal of measuring relative forms of objective novelty or surprisingness of each individual rule with respect to other rules that hold in the same dataset. We measure novelty through the extent to which the confidence value is robust, taken relative to the confidences of related (for instance, logically stronger) rules, as opposed to the absolute consideration of the single rule at hand. We consider two variants of this idea and analyze their logical and algorithmic properties. Since this approach has the drawback of requiring further parameters, we also propose a framework in which the user sets a single parameter, of quite clear intuitive semantics, from which the corresponding thresholds for confidence and novelty are computed.