Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Mining Association Rules in Preference-Ordered Data
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Classification Strategies Using Certain and Possible Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Variable Consistency Model of Dominance-Based Rough Sets Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Rough Set Approach to Decisions under Risk
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Dominance-Based Rough Set Approach Using Possibility and Necessity Measures
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Variable Consistency Monotonic Decision Trees
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Modelling Complex Patterns by Information Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Spatio-Temporal Approximate Reasoning over Complex Objects
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization
Multiobjective Optimization
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Rough sets and information granulation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Descriptors and templates in relational information systems
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Tolerance based templates for information systems: foundations and perspectives
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Rough sets: trends and challenges
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Dominance-based rough set approach for possibilistic information systems
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables
International Journal of Approximate Reasoning
Knowledge discovery by relation approximation: a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Spiral multi-aspect hepatitis data mining
AM'03 Proceedings of the Second international conference on Active Mining
Approximation spaces and information granulation
Transactions on Rough Sets III
Arrow decision logic for relational information systems
Transactions on Rough Sets V
Modelling Complex Patterns by Information Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Spatio-Temporal Approximate Reasoning over Complex Objects
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Do impression management tactics and/or supervisor-subordinate guanxi matter?
Knowledge-Based Systems
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The paper is devoted to knowledge discovery from data, taking into account prior knowledge about preference semantics in patterns to be discovered. The data concern a set of situations (objects, states, examples) described by a set of attributes (properties, features, characteristics). The attributes are, in general, divided into condition and decision attributes, corresponding to input and output of a situation. The situations are partitioned by decision attributes into decision classes. A pattern discovered from the data has a symbolic form of decision rule or decision tree. In many practical problems, some condition attributes are defined on preference-ordered scales and the decision classes are also preference-ordered. The known methods of knowledge discovery ignore, unfortunately, this preference information, taking thus a risk of drawing wrong patterns. To deal with preference-ordered data we propose to use a new approach called Dominance-based Rough Set Approach (DRSA). Given a set of situations described by at least one condition attribute with preference-ordered scale and partitioned into preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations. The rough approximation of this partition is a starting point for induction of "if..., then..." decision rules. The syntax of these rules is adapted to represent preference orders. The DRSA analyses only facts present in data and possible inconsistencies are identified. It preserves the concept of granular computing, however, the granules are dominance cones in evaluation space, and not bounded sets. It is also concordant with the paradigm of computing with words, as it exploits ordinal, and not necessarily cardinal, character of data.