A rough set approach to attribute generalization in data mining
Information Sciences: an International Journal
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Quantitative approaches for information modeling
Quantitative approaches for information modeling
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Dominance-based rough set approach and knowledge reductions in incomplete ordered information system
Information Sciences: an International Journal
Incremental Maintenance of Online Summaries Over Multiple Streams
IEEE Transactions on Knowledge and Data Engineering
Interval ordered information systems
Computers & Mathematics with Applications
Information Sciences: 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
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
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Incremental and General Evaluation of Reverse Nearest Neighbors
IEEE Transactions on Knowledge and Data Engineering
International Journal of Intelligent Systems
The incremental method for fast computing the rough fuzzy approximations
Data & Knowledge Engineering
Incremental learning optimization on knowledge discovery in dynamic business intelligent systems
Journal of Global Optimization
Combination entropy and combination granulation in incomplete information system
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
Set-valued information systems
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Dominance-Based rough set approach to case-based reasoning
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
A parallel method for computing rough set approximations
Information Sciences: an International Journal
LEAD: a methodology for learning efficient approaches to medical diagnosis
IEEE Transactions on Information Technology in Biomedicine
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Neighborhood rough sets for dynamic data mining
International Journal of Intelligent Systems
Incomplete Information System and Rough Set Theory: Models and Attribute Reductions
Incomplete Information System and Rough Set Theory: Models and Attribute Reductions
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Set-valued information systems are important type of data tables and generalized models of single-valued information systems. Approximations are the focal point of approaches to knowledge discovery based on rough set theory, which can be used to extract and represent the hidden knowledge in the form of decision rules. Attribute generalization refers to dynamic change of the attribute set in an information system with respect to the requirements of real-life applications. In this paper, we focus on maintaining approximations dynamically in set-valued ordered decision systems under the attribute generalization. Firstly, a matrix-based approach for computing approximations of upward and downward unions of decision classes is constructed by introducing the dominant and dominated matrices with respect to the dominance relation. Then, incremental approaches for updating approximations are proposed, which involves several modifications to relevant matrices without having to retrain from the start on all accumulated training data. Finally, comparative experiments on data sets from UCI as well as synthetic data sets show the proposed incremental updating methods are efficient and effective for dynamic attribute generalization.