Mineração de padrões sequenciais e geração de regras de associação envolvendo temporalidade

• Ano de defesa: 2015
• Autor(a) principal: João, Rafael Stoffalette
• Orientador(a): Nicoletti, Maria do Carmo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Mineração de padrões sequenciais; Regras de associação; Tratamento de temporalidade
• Palavras-chave em Inglês: Sequential-pattern data mining; Association rules; Temporal reasoning
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8923

Data mining aims at extracting useful information from a Database (DB). The mining process enables, also, to analyze the data (e.g. correlations, predictions, chronological relationships, etc.). The work described in this document proposes an approach to deal with temporal knowledge extraction from a DB and describes the implementation of this approach, as the computational system called S_MEMIS+AR. The system focuses on the process of finding frequent temporal patterns in a DB and generating temporal association rules, based on the elements contained in the frequent patterns identified. At the end of the process performs an analysis of the temporal relationships between time intervals associated with the elements contained in each pattern using the binary relationships described by the Allen´s Interval Algebra. Both, the S_MEMISP+AR and the algorithm that the system implements, were subsidized by the Apriori, the MEMISP and the ARMADA approaches. Three experiments considering two different approaches were conducted with the S_MEMISP+AR, using a DB of sale records of products available in a supermarket. Such experiments were conducted to show that each proposed approach, besides inferring new knowledge about the data domain and corroborating results that reinforce the implicit knowledge about the data, also promotes, in a global way, the refinement and extension of the knowledge about the data.