Concept lattices associated with L-Fuzzy W-contexts

\begin{abstract} \noindent We generalize in this paper the $L$-Fuzzy concept theory we developed in a previous paper ([1]), using the composition of $L$-Fuzzy relations. This theory models knowledge acquisition and classification and takes as departure point Wille's idea ([5]). We begin the work defining $L$-Fuzzy W-contexts as the tuples (L,W,X,Y, $\underset{\textstyle \backsim}{R}$) where W, X and Y are the sets of labels, objects and attributes respectively, and $\underset{\textstyle \backsim}{R}\in L^{X\times Y}$ is an $L$-Fuzzy relation. From these contexts, we will give the operators needed to define the \linebreak $L$-Fuzzy W-concepts. These concepts will be pairs of relations ($\underset{\textstyle \backsim}{P}, \underset{\textstyle \backsim}{Q}$) where $\underset{\textstyle \backsim}{P}\in L^{W\times X}$, $\underset{\textstyle \backsim}Q\in L^{W\times Y}$ satisfying $\underset{\textstyle \backsim}{P_{1}}= \underset{\textstyle \backsim}Q$ and $\underset{\textstyle \backsim}Q_{2}= \underset{\textstyle \backsim}P$ with the operator 1 and 2 definitions given. After proving the lattice structure of the $L$-Fuzzy W-concepts set, we analyse a practical example where we interpret the new concept definition \bigskip \noindent {\bf Key words:} $L$-Fuzzy concepts, $L$-Fuzzy W-concepts, $L$-Fuzzy W-contexts, Conceptual knowledge. \end{abstract

The use of two relations in L-fuzzy contexts

In the analysis of relations among the elements of two sets it is usual to obtain different values depending on the point of view from which these relations are measured. The main goal of the paper is the modelization of these situations by means of a generalization of the L-fuzzy concept analysis called L-fuzzy bicontext. We study the L-fuzzy concepts of these L-fuzzy bicontexts obtaining some interesting results. Specifically, we will be able to classify the biconcepts of the L-fuzzy bicontext. Finally, a practical case is developed using this new tool.This work has been partially supported by the Research Group “Intelligent Systems and Energy (SI+E)” of the Basque Government, under Grant IT677-13, by the Research Groups “Artificial Intelligence and Approximate Reasoning” and “Adquisición de conocimiento y minería de datos, funciones especiales y métodos numéricos avanzados” of the Public University of Navarra and by project TIN2013-40765-P