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Table 1 Performance of each algorithm on three synthetic data sets after 500 trials, with the initial number of Gaussian components is set as k =20, where ‘a’ indicates the best within its column

From: Projection-embedded BYY learning algorithm for Gaussian mixture-based clustering

Data set

GMM-a

GMM-b

GMM-c

Algorithms

CSR

VI

PRI

CSR

VI

PRI

CSR

VI

PRI

VB-DNW

0.4660

1.0243

0.7730

0.5160

0.6264

0.8599

0.1060

1.3337

0.6469

MML-Jef

0.1700

3.2637

0.7345

0.1600

4.8235

0.7573

0.4140

58.0039

0.6388

BYY-Jef

0.2167

1.1135

0.7006

0.5533

0.6650

0.8257

0.0100

1.6889

0.4732

BYY-DNW

0.1433

1.1947

0.7039

0.0700

0.5373 a

0.8760

0

1.7948

0.4622

pBYY

0.7260 a

0.5852 a

0.8692 a

0.8840 a

0.5482

0.8779 a

0.6100 a

1.1328 a

0.7451 a

  1. For a good performance, we expect that the values of CSR and PRI are big and that the VI value is small.