Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Measuring the VC-dimension of a learning machine
Neural Computation
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Multivariate discretization of continuous variables for set mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Discretization of Continuous Attributes for Learning Classification Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
A MINSAT Approach for Learning in Logic Domains
INFORMS Journal on Computing
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Multivariate supervised discretization, a neighborhood graph approach
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Multivariate Interdependent Discretization for Continuous Attribute
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
A Fuzzy Approach to Partitioning Continuous Attributes for Classification
IEEE Transactions on Knowledge and Data Engineering
Data mining logic explanations from numerical data
Data mining logic explanations from numerical data
Data Discretization Unification
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Weighted proportional k-interval discretization for naive-Bayes classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications
A multivariate discretization method for learning Bayesian networks from mixed data
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Subgroup mining for interactive knowledge refinement
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
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A number of two-class classification methods first discretize each attribute of two given training sets and then construct a propositional DNF formula that evaluates to True for one of the two discretized training sets and to False for the other one. The formula is not just a classification tool but constitutes a useful explanation for the differences between the two underlying populations if it can be comprehended by humans and is reliable. This paper shows that comprehensibility as well as reliability of the formulas can sometimes be improved using a discretization scheme where linear combinations of a small number of attributes are discretized.