Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough set algorithms in classification problem
Rough set methods and applications
Rough set methods for the synthesis and analysis of concurrent processes
Rough set methods and applications
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
Efficient Discovery of Functional Dependencies and Armstrong Relations
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Searching for Frequential Reducts in Decision Tables with Uncertain Objects
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Association reducts: complexity and heuristics
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
On a Criterion of Similarity between Partitions Based on Rough Set Theory
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Transactions on rough sets XII
Uncertainty and feature selection in rough set theory
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
An efficient rough feature selection algorithm with a multi-granulation view
International Journal of Approximate Reasoning
Attribute reduction for dynamic data sets
Applied Soft Computing
Attribute reduction: A dimension incremental strategy
Knowledge-Based Systems
An accelerator for attribute reduction based on perspective of objects and attributes
Knowledge-Based Systems
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In this paper, we present our Fun algorithm for discovering minimal sets of conditional attributes functionally determining a given dependent attribute. In particular, the algorithm is capable of discovering Rough Sets certain, generalized decision, and membership distribution reducts. Fun can operate either on partitions of objects or alternatively on stripped partitions, which do not store singleton groups. It is capable of using functional dependencies occurring among conditional attributes for pruning candidate dependencies. In this paper, we offer further reduction of stripped partitions, which allows correct determination of minimal functional dependencies provided optional candidate pruning is not carried out. In the paper we consider six variants of Fun , including two new variants using reduced stripped partitions. We have carried out a number of experiments on benchmark data sets to test the efficiency of all variants of Fun . We have also tested the efficiency of the Fun 's variants against the Rosetta and RSES toolkits' algorithms computing all reducts and against Tane , which is one of the most efficient algorithms computing all minimal functional dependencies. The experiments prove that Fun is up to 3 orders of magnitude faster than the the Rosetta and RSES toolkits' algorithms and faster than Tane up to 30 times.