Association Rules Selection Using Terminological Ontologies

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Provided by: International Journal of Computer Science & Engineering Technology (IJCSET)
Topic: Big Data
Format: PDF
Huge volume of discovered association rules from the database, limits the usefulness of it. Generally based on statistical information, all the extracted rules are not interesting to the user and it is difficult to analyze manually. To overcome this drawback, efficient post-processing task is used to integrate the user knowledge. Thus, it is crucial to help the decision-maker with an efficient reducing rule number. Hence, to prune and filter discovered rules a new interactive approach is used. In post processing step, ontology's and rule schemas supervise association rule mining.
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