On changing continuous attributes into ordered discrete attributes
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
A new version of the rule induction system LERS
Fundamenta Informaticae
Multivariate discretization of continuous variables for set mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Handling Continuous Attributes in Discovery of Strong Decision Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
A Comparison of Six Discretization Algorithms Used for Prediction of Melanoma
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Data reduction: discretization of numerical attributes
Handbook of data mining and knowledge discovery
Unsupervised discretization using kernel density estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incomplete data and generalization of indiscernibility relation, definability, and approximations
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Mining numerical data – a rough set approach
Transactions on Rough Sets XI
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We present an approach to mining numerical data based on rough set theory using calculus of attribute-value blocks. An algorithm implementing these ideas, called MLEM2, induces high quality rules in terms of both simplicity (number of rules and total number of conditions) and accuracy. Additionally, MLEM2 induces rules not only from complete data sets but also from data with missing attribute values, with or without numerical attributes.