Uma Metodologia para Mineração de Regras de Associação Usando Ontologias para Integração de Dados Estruturados e Não-Estruturados
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2010-08-23
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Universidade Federal de Goiás
Resumo
Data and text mining methods have been applied in several areas of knowledge with the purpose of extracting useful information from large data volumes. Among the various data
mining methods reported by specialized literature, association rule mining has proved useful in producing understandable rules. However, one of its major problems is the significant amount of rules produced, which hampers the selection of the more relevant rules needed to reply to a query. This study proposes a method for mining data from structured and unstructured sources in order to generate association rules between the terms extracted. The process of mining data from unstructured sources is assisted by an ontology that maps knowledge from a specific domain. The result of such process is converted into structured data and combined with data from other structured sources. A combination of objective and subjective interest measures is used to filter the set of rules obtained, in addition to support and confidence model. To verify the feasibility of this method in real-life situations, it was applied to a database of police occurrence reports of a government institution, which included data stored in structured and unstructured sources.
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CAMILO, Cassio Oliveira. A Methodology for Mining Association Rules Using Ontologies for Integrating Structured and Non-Structured Data. 2010. 149 f. Dissertação (Mestrado em Ciências Exatas e da Terra - Ciências da Computação) - Universidade Federal de Goiás, Goiânia, 2010.