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Title:
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ONTOLOGY IN ASSOCIATION RULES PRE-PROCESSING AND POST-PROCESSING |
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Author(s):
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Inhauma Neves Ferraz , Ana Cristina Bicharra Garcia |
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ISBN:
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978-972-8924-63-8 |
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Editors:
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Hans Weghorn and Ajith P. Abraham |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Data Mining, Association Rules, Ontology, Preprocessing, Post processing, Pruning |
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Type:
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Short Paper |
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First Page:
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87 |
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Last Page:
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91 |
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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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Data mining has emerged to address the problem of transforming data into useful knowledge. Although most data mining
techniques, such as Association Rules, substantially reduce the search space, oftentimes one finds that the solution
obtained surpasses the human ability to handle the resulting information. Furthermore, a good part of the information in
repositories may be wrongfully dismissed due to the mining methodsÂ’ inability to grasp the relationships between stored
data from world knowledge that makes it possible to discover new valuable results, as well as eliminate irrelevant ones.
This paper studies domain ontology as an instrument to enhance the mining results of Association Rules, which also acts
to reduce the number of generated association rules. The adopted model is based on generalization and specialization
processes in which the rules are filtered by metrics based on the coverage and confidence indicators. |
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