Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Machine Learning
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
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
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble
IEEE Transactions on Information Technology in Biomedicine
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Breast cancer survivability via AdaBoost algorithms
HDKM '08 Proceedings of the second Australasian workshop on Health data and knowledge management - Volume 80
Temporal pattern discovery for trends and transient effects: its application to patient records
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction
Advanced Web and NetworkTechnologies, and Applications
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Toward breast cancer survivability prediction models through improving training space
Expert Systems with Applications: An International Journal
Learning Approximate Sequential Patterns for Classification
The Journal of Machine Learning Research
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Adverse drug reaction mining in pharmacovigilance data using formal concept analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Robust discovery of local patterns: subsets and stratification in adverse drug reaction surveillance
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
International Journal of Data Warehousing and Mining
A Roadmap for Designing a Personalized Search Tool for Individual Healthcare Providers
Journal of Medical Systems
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In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.