Theoretical foundations of order-based genetic algorithms
Fundamenta Informaticae - Special issue: to the memory of Prof. Helena Rasiowa
Fast discovery of association rules
Advances in knowledge discovery and data mining
Fundamenta Informaticae
Rough sets and association rule generation
Fundamenta Informaticae
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Fundamenta Informaticae
Interactive Gene Clustering--A Case Study of Breast Cancer Microarray Data
Information Systems Frontiers
Ensembles of Classifiers Based on Approximate Reducts
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Association reducts: boolean representation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Association reducts: a framework for mining multi-attribute dependencies
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent 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
Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts
Transactions on Computational Science V
Association reducts: boolean representation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
| Hi-index | 0.00 |
We investigate association reducts, which extend previously studied information and decision reducts in capability of expressing dependencies between groups of attributes in data. We formulate optimization problems related to the most informative associations between groups of attributes. We provide heuristic mechanisms for addressing those problems. We also discuss at more general level how to express approximate dependencies between groups of attributes.