Generación de reglas estadísticas a partir de grandes bases de datos
保存先:
| 著者: | , , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 1994 |
| その他の書誌記述: | 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. |
| 国: | Portal de Revistas UCR |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| 言語: | Español |
| OAI Identifier: | oai:archivo.portal.ucr.ac.cr:article/106 |
| オンライン･アクセス: | https://archivo.revistas.ucr.ac.cr/index.php/matematica/article/view/106 |