Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Learning Logical Definitions from Relations
Machine Learning
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Mining Association Rules in Preference-Ordered Data
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
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
An Incremental Rule Induction Algorithm Based on Ordering Relations
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
The concept of reducts in pawlak three-step rough set analysis
Transactions on Rough Sets XVI
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The main objective of this paper is to provide a granular computing based interpretation of rules representing two levels of knowledge. This is done by adopting and adapting the decision logic language for granular computing. The language provides a formal method for describing and interpreting conditions in rules as granules and rules as relationships between granules. An information table is used to construct a concrete granular computing model. Two types of granules are constructed from an information table. They lead to two types of rules called low order and high order rules. As examples, we examine rules in the standard rough set analysis and dominance-based rough set analysis.