TY - GEN
T1 - Generating an informative cover for association rules
AU - Cristofor, Laurentiu
AU - Simovici, Dan
PY - 2002
Y1 - 2002
N2 - 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 Guigues-Duquenne-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.
AB - 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 Guigues-Duquenne-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.
UR - https://www.scopus.com/pages/publications/78149312575
UR - https://www.scopus.com/pages/publications/78149312575#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:78149312575
SN - 0769517544
SN - 9780769517544
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 597
EP - 600
BT - Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
T2 - 2nd IEEE International Conference on Data Mining, ICDM '02
Y2 - 9 December 2002 through 12 December 2002
ER -