C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Artificial Intelligence Review
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Interactive search of rules in medical data using multiobjective evolutionary algorithms
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
A PSO/ACO approach to knowledge discovery in a pharmacovigilance context
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Compression-Based Measures for Mining Interesting Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Towards the Generic Framework for Utility Considerations in Data Mining Research
Proceedings of the 2010 conference on Data Mining for Business Applications
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Performance of classification models from a user perspective
Decision Support Systems
Interestingness measures for fixed consequent rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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In the last few years, the data mining community has proposed a number of objective rule interestingness measures to select the most interesting rules, out of a large set of discovered rules. However, it should be recalled that objective measures are just an estimate of the true degree of interestingness of a rule to the user, the so-called real human interest. The latter is inherently subjective. Hence, it is not clear how effective, in practice, objective measures are. More precisely, the central question investigated in this paper is: “how effective objective rule interestingness measures are, in the sense of being a good estimate of the true, subjective degree of interestingness of a rule to the user?” This question is investigated by extensive experiments with 11 objective rule interestingness measures across eight real-world data sets.