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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Rough Sets and Association Rule Generation
Fundamenta Informaticae
Optimizations of Rough Set Model
Fundamenta Informaticae
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Searching for patterns is one of the main goals in data mining. Patterns have important applications in many KDD domains like rule extraction or classification. In this paper we present some methods of rule extraction by generalizing the existing approaches for the pattern problem. These methods, called partition of attribute values or grouping of attribute values, can be applied to decision tables with symbolic value attributes. If data tables contain symbolic and numeric attributes, some of the proposed methods can be used jointly with discretization methods. Moreover, these methods are applicable for incomplete data. The optimization problems for grouping of attribute values are either NP-complete or NP-hard. Hence we propose some heuristics returning approximate solutions for such problems.