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Title:
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MINING ASSOCIATION RULES USING ONTOLOGIES FROM STRUCTURED AND UNSTRUCTURED DATA |
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Author(s):
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Cássio Oliveira Camilo, João Carlos da Silva, Auri Marcelo Rizzo Vincenzi, Cedric Luiz de Carvalho |
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ISBN:
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978-989-8533-01-2 |
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Editors:
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Bebo White, Pedro Isaías and Flávia Maria Santoro |
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Year:
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2011 |
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Edition:
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Single |
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Keywords:
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Data mining, Association rules, Ontologies, Concepts, Text mining. |
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Type:
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Full Paper |
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First Page:
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325 |
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Last Page:
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332 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The diffusion of online resources and internet communication facilities has contributed to the daily growth of available information stored in structured and unstructured sources. Consequently, the demand for extraction of useful information from heterogeneous sources has led organizations to use data and text mining techniques, among which association rule mining is widely used. However, one of the major problems arising from the use of large data sources is the considerable amount of rules produced, which makes manual analysis of data patterns a highly complex task. Another challenge is the semantic (or concept) extraction of unstructured text data in a way that it may combine with structured data. Therefore, this study proposes a method for mining association rules from structured and unstructured data. During this process, ontology is used for semantic interpretation of unstructured data. To determine its feasibility in real-life situations, the proposed method was applied to a database of police reports of a government institution. |
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