Mining bases for association rules using closed sets
Résumé
Association rules are conditional implications between requent itemsets. The problem of the usefulness and the elevance of the set of discovered association rules is related to the huge number of rules extracted and the presence of many redundancies among these rules for many datasets. We address this important problem using the Galois connection framework and we show that we can generate bases or association rules using the frequent closed itemsets extracted by the Close or the A-Close algorithms.
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Mining_Bases_for_Association_Rules_using_Closed_Sets_Taouil_et_al._ICDE_2000.pdf (21.14 Ko)
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