Recent Issues on Fuzzy Databases by Patrick Bosc, Laurence Duval, Olivier Pivert (auth.), Dr.

By Patrick Bosc, Laurence Duval, Olivier Pivert (auth.), Dr. Gloria Bordogna, Dr. Gabriella Pasi (eds.)

This publication makes a speciality of the new learn matters concerning the program of fuzzy set idea to increase the functionalities of database administration systems.
During the prior 5 years, the study during this box has moved from a in basic terms theoretical framework in most cases addressing the definition of fuzzy extensions of the relational database version to the dignity of alternative, object-oriented database paradigms, additionally in relation with their implementation and alertness in particular contexts (ex. geographic info systems), fuzzy facts mining, and fuzzy practical dependencies definition.
Besides contributing to stimulate the curiosity within the box of fuzzy databases, the ebook has the purpose of revealing that the examine performed to date has matured a few fuzzy extensions of classical databases which are possible to be applied and utilized fruitfully in genuine applications.

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40 4 Concluding remarks We have shown a flexible division operator in a possibility-based fuzzy relational model based on necessity and possibility measures. l])/ts[As] is compatible with quantifier Q. The approach has no restrictions to types of quantifiers, although the methods proposed so far do so. This is very significant, because users can use the flexible division operator without paying attention to what type quantifiers belong to. Acknowledgments The author wishes to thank the anonymous reviewers for their comments, which were valuable in improving the quality of the final version.

Classically, mining generalized association rules is to discover the relationships between data attributes upon all levels of presumed exact taxonomic structures. In many real-world applications, however, the taxonomic structures may not be crisp but fuzzy. This paper focuses on the issue of mining generalized association rules with fuzzy taxonomic structures. First, fuzzy extensions are made to the notions of the degree of support, the degree of confidence, and the R-interest measure. The computation of these degrees takes into account the fact that there may exist a partial belonging between any two itemsets in the taxonomy concerned.

Let us denote by Res the resemblance relation expressing fuzzy equality between the values of domain D. The interchangeability degree related to the pair (A(x), A(y)) with respect to Res is the degree to which A(x) can be replaced with A(y) and reciprocally: IlINT(A(x), A(y)) = min(llrepl(A(x), A(y)), Ilrepl(A(y), A(x))). An imprecise value A(x) can be replaced with another imprecise value A(y) if, for each representative

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