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
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
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
Mining Numerical Data--A Rough Set Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Unsupervised discretization using kernel density estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial 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
Uncertainty measures for rough formulae in rough logic: An axiomatic approach
Computers & Mathematics with Applications
Rough Truth Degrees of Formulas and Approximate Reasoning in Rough Logic
Fundamenta Informaticae
Rough Truth Degrees of Formulas and Approximate Reasoning in Rough Logic
Fundamenta Informaticae
A fuzzy-rough sets based compact rule induction method for classifying hybrid data
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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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. MLEM2 induces rules not only from complete data sets but also from data with missing attribute values, with or without numerical attributes. Additionally, we present experimental results on a comparison of three commonly used discretization techniques: equal interval width, equal interval frequency and minimal class entropy (all three methods were combined with the LEM2 rule induction algorithm) with MLEM2. Our conclusion is that even though MLEM2 was most frequently a winner, the differences between all four data mining methods are statistically insignificant.