Aprimoramento do modelo de seleção dos padrões associativos: uma abordagem de mineração de dados
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2021-12-20
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Universidade Federal de Goiás
Resumo
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.
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Citação
RODRIGUES, D. C. Aprimoramento do modelo de seleção dos padrões associativos: uma abordagem de mineração de dados. 2021. 184 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021.