Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute

Authors:
Marzena Kryszkiewicz;Piotr Lasek
Affiliations:
Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland;Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

Citing 9
Cited 3

Fast discovery of association rules

Advances in knowledge discovery and data mining
Rough set algorithms in classification problem

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
Certain, Generalized Decision, and Membership Distribution Reducts Versus Functional Dependencies in Incomplete Systems

RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms

FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute

Transactions on Rough Sets IX
Information Granulation: A Medical Case Study

Transactions on Rough Sets IX
On the relation between jumping emerging patterns and rough set theory with application to data classification

Transactions on rough sets XII

Quantified Score

Hi-index	0.00

Visualization

Abstract

In our paper, we offer an efficient Funalgorithm for discovering minimal sets of conditional attributes functionally determining a given dependent attribute, and in particular, for discovering Rough Sets certain, generalized decision, and membership distribution reducts. Funcan operate either on partitions or alternatively on stripped partitions that do not store singleton groups. It is capable of using functional dependencies occurring among conditional attributes for pruning candidate dependencies. The experimental results show that all variants of Funhave similar performance. They also prove that Funis much faster than the Rosetta toolkit's algorithms computing all reducts and faster than TANE, which is one of the most efficient algorithms computing all minimal functional dependencies.