Data Mining and Machine Oriented Modeling: A Granular Computing Approach
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RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Selecting Samples and Features for SVM Based on Neighborhood Model
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
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WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
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RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
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Transactions on computational science II
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Information Sciences: an International Journal
Neighborhood outlier detection
Expert Systems with Applications: An International Journal
FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices
Computers & Mathematics with Applications
A roadmap from rough set theory to granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Table representations of granulations revisited
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Set-valued information systems
Information Sciences: an International Journal
Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system
Information Sciences: an International Journal
On Modal and Fuzzy Decision Logics Based on Rough Set Theory
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First-Order Rough Logic I: Approximate Reasoning Via Rough Sets
Fundamenta Informaticae
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
A Comparative Study of Ordered and Covering Information Systems
Fundamenta Informaticae
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Queries in database can be classified roughly into two types: specific targets and fuzzy targets. Many queries are in effect fuzzy targets, however, because of lacking the supports, the user has been emulating them with specific targets by retiring a query repeatedly with minor changes. In this paper, we augment the relational database, with neighborhood systems, so the database can answer a fuzzy query. There have been many works to combine relational databases and fuzzy theory. Bucklles and Petry replaced attributes values by sets of values. Zemankova-Leech, Kandel, and Zviell used fuzzy logic. The formalism of present work is quite general, it allows numerical or nonnumerical measurements of fuzziness in relational databases. The fuzzy theory present here is quite different from the usual theory. Our basic assumption here is that: the data are not fuzzy, the queries are.Motro [Motr86] introduced the notion of distance into the relational databases. From that he can, then, define the notion of “close-ness” and develop goal queries. Though “distance” is a useful concept, yet very often the quantification of it is meaningless or extremely difficult. For example, “very close”, “very far” are meaningful concept of distance, yet there is no practical way to quantity them for all occasions. Our approach here is more direct, we define directly the meaning of “very close neighborhood”. Using the concept of neighborhoods is not very original, in fact, in the theory of topological spaces [Dugu66], mathematician has been using the “neighborhood system” to study the phenomena of “close-ness”. In the territory of fuzzy queries, the notion of “neighborhood” captures the essence of the qualitative information of “close-ness” better than the brute-force-quantified information (distance). A “fuzzy” neighborhood is a qualitative measure of fuzziness.On the surface, it seems a very complicated procedure to define a neighborhood for each value in the attribute. In fact, if we use the characteristic function (membership function) to define a subset, then the defining procedure is merely another type of distance function (non-measure distance or symbolic distance). Now, to define the neighborhood system one can simply re-entered the third column of the relation with linguistic values: “very close”, “close”, “far”. Note that there is a “greater than” relation among these linguistic values. In mathematical terms, they forms a lattice [Jaco60]. For technical reason, we require the values in third column be elements of a lattice. Note that real number is a lattice, so we get Motro's results back.