Explorando a técnica de indexação de conjuntos candidatos na mineração de conjuntos freqüentes

• Ano de defesa: 2008
• Autor(a) principal: Prado, Adriana Bechara
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Programa de Pós-Graduação em Computação; Computação
• Palavras-chave em Português: Ciência da computação; Processo de mineração de dados; Regras de associação relacionais; Computer science; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO::COMPUTABILIDADE E MODELOS DE COMPUTACAO
• Link de acesso: https://app.uff.br/riuff/handle/1/17808

During the last ten years, many algorithms have been proposed to mine frequent itemsets. In order to fairly evaluate their behavior, the IEEE/ICDM Workshop on Frequent Itemset Mining Implementations (FIMI03) has been recently organized. According to its analysis, kDCI++ is a state-of-the-art algorithm. However, it can be observed from the FIMI 03 experiments that its efficient behavior does not occur for low minimum supports on sparse databases. Aiming at improving kDCI++ and making it even more competitive, we present the kDCI-3 algorithm. This proposal directly accesses candidates not only in the two initial iterations but specially in the third one, which represents, in general, the highest computational cost of kDCI++ for low minimum supports. Results have shown that kDCI-3 outperforms kDCI++ in the conducted experiments. When compared to other important algorithms, kDCI-3 enlarged the number of times kDCI++ presented the best behavior.