Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Information-theoretic measures of uncertainty for rough sets and rough relational databases
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fuzzy reasoning model under quotient space structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Dealing with uncertainty in data mining and information extraction
An improved accuracy measure for rough sets
Journal of Computer and System Sciences
Measures for evaluating the decision performance of a decision table in rough set theory
Information Sciences: an International Journal
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
An initialization method for the K-Means algorithm using neighborhood model
Computers & Mathematics with Applications
Generalized lower and upper approximations in a ring
Information Sciences: an International Journal
A new uncertainty measure of rough sets
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Information Sciences: an International Journal
A data labeling method for clustering categorical data
Expert Systems with Applications: An International Journal
A framework for clustering categorical time-evolving data
IEEE Transactions on Fuzzy Systems
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
A two-grade approach to ranking interval data
Knowledge-Based Systems
A comparative study of rough sets for hybrid data
Information Sciences: an International Journal
A cluster centers initialization method for clustering categorical data
Expert Systems with Applications: An International Journal
Entropy and co-entropy of a covering approximation space
International Journal of Approximate Reasoning
Rough set theory applied to lattice theory
Information Sciences: an International Journal
A measurement theory view on the granularity of partitions
Information Sciences: an International Journal
An Axiomatic Approach to the Roughness Measure of Rough Sets
Fundamenta Informaticae
Some properties of generalized rough sets
Information Sciences: an International Journal
Can fuzzy entropies be effective measures for evaluating the roughness of a rough set?
Information Sciences: an International Journal
An accelerator for attribute reduction based on perspective of objects and attributes
Knowledge-Based Systems
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
The modeling of time series based on fuzzy information granules
Expert Systems with Applications: An International Journal
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In rough set theory, accuracy and roughness are used to characterize uncertainty of a set and approximation accuracy is employed to depict accuracy of a rough classification. Although these measures are effective, they have some limitations when the lower/upper approximation of a set under one knowledge is equal to that under another knowledge. To overcome these limitations, we address in this paper the issues of uncertainty of a set in an information system and approximation accuracy of a rough classification in a decision table. An axiomatic definition of knowledge granulation for an information system is given, under which these three measures are modified. Theoretical studies and experimental results show that the modified measures are effective and suitable for evaluating the roughness and accuracy of a set in an information system and the approximation accuracy of a rough classification in a decision table, respectively, and have a much simpler and more comprehensive form than the existing ones.