%0 article
%A Schektman, Yves
%E Trejos Zelaya, Javier
%E Troupé, Marylène
%D 2009
%G spa
%T Generación de reglas estadísticas a partir de grandes bases de datos
%U https://revistas.ucr.ac.cr/index.php/matematica/article/view/106
%X Given a set of categorical variables, we want to predict one or more of them by the way rules. We propose an algorithm that (i) is guided by statistical results in a relational geometry where we use assymetrical association indices, and (ii) makes statistical and euclidian approximations. The iterative method we propose can obtain rules without introducing a priori their premises in the set of independent conjonctions analized by the generator at each step. The algorithm has a linear complexity with regard to the number of individual; this property makes it suitable for large data sets. We present results over data examples.