Flexible Querying Techniques Based on CBR

Flexible Querying Techniques Based on CBR

Guy De Tré, Marysa Demoor, Bert Callens, Lise Gosseye
ISBN13: 9781599048536|ISBN10: 1599048531|EISBN13: 9781599048543
DOI: 10.4018/978-1-59904-853-6.ch007
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MLA

De Tré, Guy, et al. "Flexible Querying Techniques Based on CBR." Handbook of Research on Fuzzy Information Processing in Databases, edited by José Galindo, IGI Global, 2008, pp. 167-190. https://doi.org/10.4018/978-1-59904-853-6.ch007

APA

De Tré, G., Demoor, M., Callens, B., & Gosseye, L. (2008). Flexible Querying Techniques Based on CBR. In J. Galindo (Ed.), Handbook of Research on Fuzzy Information Processing in Databases (pp. 167-190). IGI Global. https://doi.org/10.4018/978-1-59904-853-6.ch007

Chicago

De Tré, Guy, et al. "Flexible Querying Techniques Based on CBR." In Handbook of Research on Fuzzy Information Processing in Databases, edited by José Galindo, 167-190. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-853-6.ch007

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Abstract

In case-based reasoning (CBR), a new untreated case is compared to cases that have been treated earlier, after which data from the similar cases (if found) are used to predict the corresponding unknown data values for the new case. Because case comparisons will seldom result in an exact-similarity matching of cases and the conventional CBR approaches do not efficiently deal with such imperfections, more advanced approaches that adequately cope with these imperfections can help to enhance CBR. Moreover, CBR in its turn can be used to enhance flexible querying. In this chapter, we describe how fuzzy set theory can be used to model a gradation in similarity of the cases and how the inevitable uncertainty that occurs when predictions are made can be handled using possibility theory resulting in what we call flexible CBR. Furthermore, we present how and under which conditions flexible CBR can be used to enhance flexible querying of regular databases.

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