Variable precision rough set model
Journal of Computer and System Sciences
A rough set approach to attribute generalization in data mining
Information Sciences: an International Journal
Uncertainly measures of rough set prediction
Artificial Intelligence
Rough approximation quality revisited
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Inclusion degree: a perspetive on measures for rough set data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Consistency-based search in feature selection
Artificial Intelligence
RRIA: a rough set and rule tree based incremental knowledge acquisition algorithm
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Dominance relation and rules in an incomplete ordered information system
International Journal of Intelligent Systems
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Measures for evaluating the decision performance of a decision table in rough set theory
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute
Transactions on Rough Sets IX
Set-valued ordered information systems
Information Sciences: an International Journal
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
A roughness measure for fuzzy sets
Information Sciences: an International Journal
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
International Journal of Intelligent Systems
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
A rough set approach to multiple dataset analysis
Applied Soft Computing
An efficient classifier design integrating rough set and set oriented database operations
Applied Soft Computing
Rough set analysis on call center metrics
Applied Soft Computing
Dynamic discreduction using Rough Sets
Applied Soft Computing
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
Incremental attribute reduction based on elementary sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A comparative study of rough sets for hybrid data
Information Sciences: an International Journal
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets
Information Sciences: an International Journal
Fundamenta Informaticae
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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Many real data sets in databases may vary dynamically. With such data sets, one has to run a knowledge acquisition algorithm repeatedly in order to acquire new knowledge. This is a very time-consuming process. To overcome this deficiency, several approaches have been developed to deal with dynamic databases. They mainly address knowledge updating from three aspects: the expansion of data, the increasing number of attributes and the variation of data values. This paper focuses on attribute reduction for data sets with dynamically varying data values. Information entropy is a common measure of uncertainty and has been widely used to construct attribute reduction algorithms. Based on three representative entropies, this paper develops an attribute reduction algorithm for data sets with dynamically varying data values. When a part of data in a given data set is replaced by some new data, compared with the classic reduction algorithms based on the three entropies, the developed algorithm can find a new reduct in a much shorter time. Experiments on six data sets downloaded from UCI show that the algorithm is effective and efficient.