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Closures and Partial Implications in Educational Data Mining

By Diego García-saiz, Marta Zorrilla and José L. Balcázar


Abstract. Educational Data Mining (EDM) is a growing field of use of data analysis techniques. Specifically, we consider partial implications. The main problems are, first, that a support threshold is absolutely necessary but setting it “right ” is extremely difficult; and, second, that, very often, large amounts of partial implications are found, beyond what an EDM user would be able to manually inspect. Our program yacaree, recently developed, is an associator that tackles both problems. In an EDM context, our program has demonstrated to be competitive with respect to the amount of partial implications output. But “finding few rules ” is not the same as “finding the right rules”. We extend the evaluation with a deeper quantitative analysis and a subjective evaluation on EDM datasets, eliciting the opinion of the instructors of the courses under analysis to assess the pertinence of the rules found by different association miners

Topics: Closure Lattices, Partial Implications, Association Rules
Year: 2014
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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