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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