Generating an informative cover for association rules

Mining association rules may generate a large numbers of rules making the results hard to analyze manually. Pasquier et al. have discussed the generation of GuiguesDuquenne–Luxenburger basis (GD-L basis). Using a similar approach, we introduce a new rule of inference and define the notion of association rules cover as a minimal set of rules that are non-redundant with respect to this new rule of inference. Our experimental results (obtained using both synthetic and real data sets) show that our covers are smaller than the GD-L basis and they are computed in time that is comparable to the classic Apriori algorithm for generating rules.

• Type: Conference
• Year: 2002
• Where: ICDM
• Authors: Laurentiu Cristofor, Dan A. Simovici
• Keywords: Association Rules, Data Mining, GD-L Basis, ICDM 2002, Pasquier Et Al