Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Mining Association Rules with Respect to Support and Anti-support-Experimental Results
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Assessing the Quality of Rules with a New Monotonic Interestingness Measure Z
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
International Journal of Applied Mathematics and Computer Science
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
Properties of rule interestingness measures and alternative approaches to normalization of measures
Information Sciences: an International Journal
Analysis of symmetry properties for bayesian confirmation measures
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Dynamic Programming Approach for Partial Decision Rule Optimization
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Rule quality measure-based induction of unordered sets of regression rules
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Confirmation measures of association rule interestingness
Knowledge-Based Systems
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
Finding Meaningful Bayesian Confirmation Measures
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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In knowledge discovery and data mining many measures of interestingness have been proposed in order to measure the relevance and utility of the discovered patterns. Among these measures, an important role is played by Bayesian confirmation measures, which express in what degree a premise confirms a conclusion. In this paper, we are considering knowledge patterns in a form of ''if..., then...'' rules with a fixed conclusion. We investigate a monotone link between Bayesian confirmation measures, and classic dimensions being rule support and confidence. In particular, we formulate and prove conditions for monotone dependence of two confirmation measures enjoying some desirable properties on rule support and confidence. As the confidence measure is unable to identify and eliminate non-interesting rules, for which a premise does not confirm a conclusion, we propose to substitute the confidence for one of the considered confirmation measures in mining the Pareto-optimal rules. We also provide general conclusions for the monotone link between any confirmation measure enjoying the desirable properties and rule support and confidence. Finally, we propose to mine rules maximizing rule support and minimizing rule anti-support, which is the number of examples, which satisfy the premise of the rule but not its conclusion (called counter-examples of the considered rule). We prove that in this way we are able to mine all the rules maximizing any confirmation measure enjoying the desirable properties. We also prove that this Pareto-optimal set includes all the rules from the previously considered Pareto-optimal borders.