Aprimoramento do modelo de seleção dos padrões associativos: uma abordagem de mineração de dados

• Ano de defesa: 2021
• Autor(a) principal: Rodrigues, Diego de Castro
• Orientador(a): Barbosa, Rommel Melgaço
• Banca de defesa: Barbosa, Rommel Melgaço, Costa, Ronaldo Martins da, Costa, Nattane Luíza da, Rocha, Marcelo Lisboa, Jorge, Lúcio de Castro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Mineração de dados; Padrões associativos; Métricas assimétricas; Regras de associação
• Palavras-chave em Inglês: Data Mining; Associative patterns; Asymmetric metrics; Association rules
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/11850

The objective of this study is to improve the association rule selection model through a set of asymmetric probabilistic metrics. We present the Health Association Rules - HAR, based on Apriori, the algorithm is composed of six functions and uses alternative metrics to the Support/Confidence model to identify the implication X → Y . Initially, the application of our solution was focused only on health data, but we realized that asymmetrical associative patterns could be applied in other contexts that seek to address the cause and effect of a pattern. Our experiments were composed of 60 real datasets taken from specialist websites, research partnerships and open data. We empirically observed the behavior of HAR in all data sets, and a comparison was performed with the classical Apriori algorithm. We realized that it has overcome the main problems of the Support/Confidence model. We were able to identify the most relevant patterns for the observed datasets, eliminating logical contradictions and redundancies. We also perform a statistical analysis of the experiments where the statistical effect is positive for HAR. HAR was able to discover more representative patterns and rare patterns, in addition to being able to perform rule grouping, filtering and ranking. Our solution presented a linear behavior in the experiments, being able to be applied in health, social, content suggestion, product indication and educational data. Not limited to these data domains, HAR is prepared to receive large amounts of data by using a customized parallel architecture.