Imprecise inference models the ability to infer sets of values or information chunks. Imprecise database inference is just as important as precise inference. In fact, it is more prevalent than its precise counterpart even in precise databases. Analyzing the extent of imprecise inference is important in knowledge discovery and database security. Imprecise inference analysis can be used to \mine"...

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and represent...

In this paper we introduce a fuzzy approach for image retrieval based on color features. Our method deals with two problems related to imprecision: the vagueness in the color description and the representation of fuzzy data in database models. To face the first problem, the concept of dominant fuzzy color is introduced to describe the image, using linguistic labels for representing the color in...

Data is often partially known, vague or ambiguous in many real world applications. To deal with such imprecise information, fuzziness is introduced in the classical model. SQLf is one of the practical language to deal with flexible fuzzy querying in Fuzzy DataBases (FDB). However, with a huge amount of fuzzy data, the necessity to work with synthetic views became a challenge for many DB communi...

A fuzzy bipolar relation is a relation defined by a fuzzy bipolar condition, which could be interpreted as an association of a constraint and a wish. In this context, the extension of the relational division operation to bipolarity is studied in this paper. Firstly, we define a bipolar division when the involved relations are crisp. Then, we define, from the semantic point of view, several form...

In this study, we first introduce the fuzzy sets, soft sets, fuzzy soft sets and their related properties. We then present the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) that is one of classical Multiple Attribute Decision Making (MADM) methods. We also present the Grey Relation Method. In the main part of this study, we extend the TOPSIS method on the fuzzy soft se...

Multidimensional association rule mining searches for interesting relationship among the values from different dimensions/attributes in a relational database. In this method the correlation is among set of dimensions i.e., the items forming a rule come from different dimensions. Therefore each dimension should be partitioned at the fuzzy set level. This paper proposes a new algorithm for genera...