Integración de modelos de agrupamiento y reglas de asociación obtenidos de múltiples fuentes de datos
Integration of Association Rules and Clustering Models Obtained from Multiple Data Sources
Daymi Morales Vega, Diana Martín Rodríguez, Ingrid Wilford Rivera y Alejandro Rosete Suárez
Instituto Superior Politécnico José Antonio Echeverría, La Habana, Cuba dmorales@ceis.cujae.edu.cu, dmartin@ceis.cujae.edu.cu, iwilford@ceis.cujae.edu.cu, rosete@ceis.cujae.edu.cu
]]>Artículo recibido el 04/02/2011.
Aceptado el 10/10/2011.
Resumen
Una alternativa posible para descubrir conocimiento sobre bases de datos distribuidas, usando técnicas de Minería de Datos, es rehusar los modelos de minería de datos locales obtenidos en cada base de datos e integrarlos para obtener patrones globales. Este proceso debe realizarse sin acceder a los datos directamente. Este trabajo se centra en la propuesta de dos métodos para la integración de modelos de Minería de Datos: Modelos de Reglas de Asociación y Agrupamiento, específicamente para reglas de asociación obtenidas usando soporte y confianza como medidas de calidad y agrupamientos basados en centroides. Estos modelos fueron obtenidos al analizar múltiples conjuntos de datos homogéneos. El estudio experimental muestra que se obtuvieron modelos globales de calidad en un tiempo razonable cuando se aumentan la cantidad de patrones locales a integrar.
Palabras clave. Integración, modelos de minería de datos, reglas de asociación, agrupamiento, Patrones.
Abstract
One possible way to discover knowledge over distributed data sources, using Data Mining techniques, is to reuse the models of local mining found in each data source and look for patterns globally valid. This process can be done without accessing the data directly. This paper focuses on the proposal of two methods for integrating data mining models: Association Rules and Clustering Models, specifically rules were obtained using support and confidence as measures of quality and clustering based on centroids. It was necessary to use metaheuristics algorithms to find a global model that is as close as possible to the local models. These models were obtained using homogeneous data sources. The experimental study showed that the proposed methods obtain global models of quality in a reasonable time when increasing the amount of local patterns to integrate.
]]> Keywords. Integration, data mining models, association rules, clustering, patterns.
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