Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough mereological foundations for design, analysis, synthesis, and control in distributed systems
Information Sciences: an International Journal - From rough sets to soft computing
Fuzzy clustering with a knowledge-based guidance
Pattern Recognition Letters
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
Rough sets and ordinal reducts
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A dynamic logistics process knowledge-based system - An RFID multi-agent approach
Knowledge-Based Systems
Dominance-based rough set approach and knowledge reductions in incomplete ordered information system
Information Sciences: an International Journal
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Stochastic dominance-based rough set model for ordinal classification
Information Sciences: an International Journal
Editorial: Special issue on soft computing for dynamic data mining
Applied Soft Computing
Interval ordered information systems
Computers & Mathematics with Applications
A dominance-based rough set approach to Kansei Engineering in product development
Expert Systems with Applications: An International Journal
A dynamic data granulation through adjustable fuzzy clustering
Pattern Recognition Letters
The Puzzle of Granular Computing
The Puzzle of Granular Computing
Evaluation of laser dynamic speckle signals applying granular computing
Signal Processing
Fuzzy rough sets with hierarchical quantitative attributes
Expert Systems with Applications: An International Journal
Constructing a decision tree from data with hierarchical class labels
Expert Systems with Applications: An International Journal
Set-valued ordered information systems
Information Sciences: an International Journal
Rule induction based on an incremental rough set
Expert Systems with Applications: An International Journal
A granular computing framework for self-organizing maps
Neurocomputing
Granular Computing and Knowledge Reduction in Formal Contexts
IEEE Transactions on Knowledge and Data Engineering
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
Variable-precision dominance-based rough set approach and attribute reduction
International Journal of Approximate Reasoning
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Hierarchical decision rules mining
Expert Systems with Applications: An International Journal
Rough set theory based on two universal sets and its applications
Knowledge-Based Systems
Data compactification and computing with words
Engineering Applications of Artificial Intelligence
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Fuzzy preference based rough sets
Information Sciences: an International Journal
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Approximation Spaces in Rough-Granular Computing
Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
International Journal of Intelligent Systems
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Granulation of Knowledge by Tools of Rough Mereology
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Analyzing IT business values - A Dominance based Rough Sets Approach perspective
Expert Systems with Applications: An International Journal
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables
International Journal of Approximate Reasoning
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
Do impression management tactics and/or supervisor-subordinate guanxi matter?
Knowledge-Based Systems
Composite rough sets for dynamic data mining
Information Sciences: an International Journal
Information Sciences: an International Journal
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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Approximations in rough sets theory are important operators to discover interesting patterns and dependencies in data mining. Both certain and uncertain rules are unraveled from different regions partitioned by approximations. In real-life applications, an information system may evolve with time by different factors such as attributes, objects, and attribute values. How to update approximations efficiently becomes vital in data mining related tasks. Dominance-based rough set approaches deal with the problem of ordinal classification with monotonicity constraints in multi-criteria decision analysis. Data missing frequently appears in the Incomplete Ordered Decision Systems (IODSs). Extended dominance characteristic relation-based rough set approaches process the IODS with two cases of missing data, i.e., ''lost value'' and ''do not care''. This paper focuses on dynamically updating approximations of upward and downward unions while attribute values coarsening or refining in the IODS. Under the extended dominance characteristic relation based rough sets, it presents the principles of dynamically updating approximations w.r.t. attribute values' coarsening and refining in the IODS and algorithms for incremental updating approximations of an upward union and downward union of classes. Comparative experiments from datasets of UCI and empirical results show the proposed method is efficient and effective in maintenance of approximations.