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Induction of ripple-down rules applied to modeling large databases
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Unifying instance-based and rule-based induction
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Advances in knowledge discovery and data mining
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Rough Sets: Theoretical Aspects of Reasoning about Data
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Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
A reduction algorithm meeting users' requirements
Journal of Computer Science and Technology
Machine Learning
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Rule + Exception Strategies for Security Information Analysis
IEEE Intelligent Systems
Tree expressions for information systems
Journal of Computer Science and Technology
“Rule + exception” strategies for knowledge management and discovery
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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In this paper we present a theoretical framework on Multilevel Data Summarization (MDS) - a process to summarize an information system into rule sets with different concise degrees (granularity) and corresponding exception sets, which is viewed as the rule-plus-exception model in cognitive science.In order to construct the theoretical framework of MDS, we propose the cognitive positive region and cognitive boundary region to substitute for the positive region and boundary region in rough set theory. Unlike current approaches, the structure of boundary region is paid more attention than positive region in this framework.Since exceptions are sometimes more important than rules for applications, we introduce a method to identify exceptions from a given information system, which concerns closely with the distribution of core attributes in the discernibility matrix.