Generación de reglas estadísticas a partir de grandes bases de datos

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 iterat...

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Main Authors: Schektman, Yves, Trejos Zelaya, Javier, Troupé, Marylène
Format: Artículo
Language: Español
Published: 2015
Online Access: http://revistas.ucr.ac.cr/index.php/matematica/article/view/106
http://hdl.handle.net/10669/12733
Summary: 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.