Mining Student Evolution Using Associative Classification and Clustering

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Provided by: IBIMA Publishing
Topic: Big Data
Format: PDF
In this paper the authors showed that using associative classification and clustering was effective in finding relations and associations between students raising among given categories. They evaluated the student progress according to associations between different factors using data collected. They concluded the performance of those groups using these two approaches, where they can mine the expected groups for each student. For future work this paper should use different categorization algorithms which handle a dynamic and updated data for the students.
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