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Table 4 Average results obtained by BD-EFEP with different combinations of objective measures for emerging pattern mining

From: Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments

Combination n r n v WRACC CONF GR TPR FPR
Jac & TPR 11.167 2.811 0.587 0.675 0.883 0.441 0.262
G-mean & Jac 13.067 3.397 0.614 0.663 0.900 0.549 0.305
Jac & FPR 13.667 4.052 0.629 0.728 0.910 0.439 0.152
G-mean & WRAcc 15.800 3.420 0.562 0.674 0.781 0.240 0.103
Jac & WRAcc 14.467 3.041 0.539 0.674 0.780 0.258 0.133
SupDiff & Jac 14.533 3.502 0.605 0.692 0.902 0.443 0.210
TPR & FPR 13.633 3.485 0.602 0.687 0.899 0.438 0.207
WRAcc & SupDiff 15.600 3.793 0.568 0.696 0.832 0.217 0.069
  1. The best result obtained for each quality measure analysed