Generación de reglas estadísticas a partir de grandes bases de datos
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Авторы: | , , |
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Формат: | artículo original |
Статус: | Versión publicada |
Дата публикации: | 1994 |
Описание: | 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. |
Страна: | Portal de Revistas UCR |
Институт: | Universidad de Costa Rica |
Repositorio: | Portal de Revistas UCR |
Язык: | Español |
OAI Identifier: | oai:portal.ucr.ac.cr:article/106 |
Online-ссылка: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/106 |
Access Level: | acceso abierto |