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Table 2 A performance comparison between PCAclust, AWclust, RFclust, and RFcluE

From: Cluster ensemble based on Random Forests for genetic data

Dataset

Measure

Methods

PCAclust

AWclust

RFclust

RFcluE

HapMap

ARI

0.5453

0.8135

0.8065

0.8282

NMI

0.7963

0.9277

0.9388

0.9353

AC

0.6326

0.8412

0.8365

0.882

AVG

0.6581

0.8608

0.8606

0.8818

Pan-Asian

ARI

0.6668

0.4631

0.4766

0.9644

NMI

0.8483

0.7663

0.749

0.962

AC

0.7314

0.6366

0.6363

0.9745

AVG

0.7488

0.622

0.6206

0.9669

Shriver’s

ARI

0.7502

0.7952

0.7795

0.8184

NMI

0.8914

0.9121

0.8758

0.9204

AC

0.8267

0.8448

0.8388

0.8989

AVG

0.8228

0.8507

0.8314

0.8792

  1. The table shows the performance of PCAclust, AWclust, RFclust, and RFcluE across the real datasets evaluated using ARI, AC, and NMI, along with an average of these three measures (AVG)