Uma estratégia de pós-processamento para seleção de regras de associação para descoberta de conhecimento

• Autor(a) principal: Cintra, Luiz Fernando da Cunha
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/0013000002msz
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/13106
• topic: Regras de associação; Pós-processamento; ARM; Agrupamento; Association rules; Post-processing; Grouping; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Association rule mining (ARM) is a traditional data mining method that provides information about associations between items in transactional databases. A known problem of ARM is the large amount of rules generated, thus requiring approaches to post-process these rules so that a human expert is able to analyze the associations found. In some contexts the domain expert is interested in investigating only one item of interest, in these cases a search guided by the item of interest can help to mitigate the problem. For an exploratory analysis, this implies looking for associations in which the item of interest appears in any part of the rule. Few methods focus on post-processing the generated rules targeting an item of interest. The present work seeks to highlight the relevant associations of a given item in order to bring knowledge about its role through its interactions and relationships in common with the other items. For this, this work proposes a post-processing strategy of association rules, which selects and groups rules oriented to a certain item of interest provided by an expert of a domain of knowledge. In addition, a graphical form is also presented so that the associations between rules and groupings of rules found are more easily visualized and interpreted. Four case studies show that the proposed method is admissible and manages to reduce the number of relevant rules to a manageable amount, allowing analysis by domain experts. Graphs showing the relationships between the groups were generated in all case studies and facilitate their analysis.