Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Objective-Oriented Utility-Based Association Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Fuzzy Sets and Systems
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Mining interestingness measures for string pattern mining
Knowledge-Based Systems
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
Properties of rule interestingness measures and alternative approaches to normalization of measures
Information Sciences: an International Journal
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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This paper considers advantages of measures of confirmation or evidential support in the context of interestingness of association rules. In particular, it is argued that the way in which they characterize positive/negative association has advantages over other measures such as null-invariant measures. Several properties are reviewed and proposed as requirements for an adequate confirmation measure in a data mining context. While none of the well-known confirmation measures satisfy all of these requirements, two new measures are proposed which do and one of these is shown to have a further advantage. Some results suggest that these measures are relatively stable when the number of null transactions varies.