ACL-Scale as a Tool for Preprocessing of Many-Valued Contexts

One of the formal technique in Data mining is Formal Concept Analysis (FCA). During preprocessing of a many-valued context many applications of FCA require the partitioning of numerical data attributes into some smaller intervals. Designation of such numerical intervals with linguistic terms without domain experts will help researchers to understand attributes and their dependencies better. To solve this task we propose the notion of a special ACL-scale, which can be considered as a linguistic variable with ordered linguistic terms, modeled by fuzzy sets. The notion of ACL-scale, algorithms of its creation and application are presented. The example how many-valued context can be transformed into formal context using ACL-scale is shown in the paper. The main contribution is a new uniform tool for preprocessing of numerical attributes of given tables which simplify their transformation into a formal context with linguistic attributes.