Uma estratégia de pós-processamento para seleção de regras de associação para descoberta de conhecimento
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2023-08-22
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Universidade Federal de Goiás
Resumo
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.
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CINTRA, L. F. C. Uma estratégia de pós-processamento para seleção de regras de associação para descoberta de conhecimento. 2023. 108 f. Dissertação (Mestrado em Ciência Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2023.