No abstract

Due to their capability of dealing with nonlinear problems, Artificial Neural Networks (ANN) are widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly represented by their connections weights. So, in order to facilitate the extraction of rules that describe the knowledge of ANN, Formal Concept Analysis (FCA) and rule extraction algorithms as the Next Closure algorithm have been used. In this work, this method is implemented on Sophiann, a computational tool that combines ANN, FCA and the rule extraction algorithms to compute the minimal implication base (Stem Base). As an example, solar energy systems are the domain application considered here, due to their importance as substitutes of traditional energy systems.

The data clustering is a technique used to make groups of objects present similar characteristics from a database. These databases may contain different variable types (numeric, categorical, scalar, binary, etc..), but categorical variables such as become a challenge clustering because lack of natural ordering. With this lack there is a big deficiency of tools and algorithms for clustering databases with categorical variables. The present work propose a new clustering algorithm for categorical data called GCA (Gower Clustering Algorithm) based in combination of algorithm TaxMap and measure of similarity coefficient of Gower. The GCA algorithm was compared with two others algorithms (clope and FarthestFirst) and through a brief statistical analysis, GCA had a very significant performance to contribute with deficiency cited.