Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
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PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Data Mining Methodology and Its Application to Semi-Automatic Knowledge Acquisition
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
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
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Constructive meta-learning with machine learning method repositories
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
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Data & Knowledge Engineering - Special issue: ER 2003
Evaluating a rule evaluation support method based on objective rule evaluation indices
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Analyzing Behavior of Objective Rule Evaluation Indices Based on a Correlation Coefficient
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
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PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
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Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
A Comparison of Composed Objective Rule Evaluation Indices Using PCA and Single Indices
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Improving a rule evaluation support method based on objective indices
International Journal of Advanced Intelligence Paradigms
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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Proceedings of the 2010 conference on Data Mining for Business Applications
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Knowledge-Based Systems
International Journal of Artificial Intelligence and Soft Computing
Investigating Accuracies of Classifications for Randomized Imbalanced Class Distributions
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Interestingness measures for fixed consequent rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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Objective: We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. Methods and materials: We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical expert's interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. Results and conclusion: The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.