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Association rules constitute a well-known and
widely employed data mining technique. We study their applicability in Educational Data
Mining. We develop a case study of datasets
from that eld: logs of an e-learning platform. We demonstrate that it is convenient to analyze such datasets in terms of association rules that relate not only presence of items in each of the transactions, but also their absence. To
cope with the algorithmic di culties and the
large output, we apply a new heuristic regarding the support of negative attributes, complementing two previously studied contributions: a basis for closure-oriented notions of redundancy and a notion of novelty called the con dence boost. Our ndings have been validated through interactions with end-user experts, namely, the instructors in whose virtual learning courses the datasets had their origin.
CitationBalcazar, J.; Tirnauca, Cristina; Zorrilla, Marta E. Mining educational data for patterns with negations and high confidence boost. A: Simposio de Teoría y Aplicaciones de Minería de Datos. "Actas de V Simposio de Teoría y Aplicaciones de Minería de Datos (TAMIDA 2010)". 2010, p. 329-338.
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