Multi-relational algorithm for mining association rules in large databases

• Autor(a) principal: Valêncio, Carlos Roberto [UNESP]
• Data de Publicação: 2011
• Outros Autores: Oyama, Fernando Takeshi [UNESP], Ichiba, Fernando Tochio [UNESP], De Souza, Rogéria Cristiane Gratão [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PDCAT.2011.56; http://hdl.handle.net/11449/72859
• topic: Association rules; Frequent itemsets mining; Multi-relational data mining; Relational database; Item sets; Large database; Memory usage; Mining associations; Multirelational data mining; Pattern mining; Relational Database; Algorithms; Data mining; Database systems

Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.