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Table 1 Dataset designed for comparing different EM algorithms in BGMM

From: A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

   cluster 1 cluster 2 cluster 3
gB alpha 15 20 25 20 20 25 15 5 1 20 1 30
  beta 20 15 20 25 20 25 15 5 20 1 30 1
bB alpha 15 10 25 20 10 5 15 12 30 25 30 35
  beta 10 15 20 25 5 10 12 15 25 30 35 30
gG mean 9 -9 11 -11 10 -10 12 -12 11 -11 13 -13
  variance 0.1 0.2 0.15 0.25 0.1 0.2 0.15 0.25 0.1 0.2 0.15 0.25
bG m mean 9.1 -9.1 11.1 -11.1 9.2 -9.2 11.2 -11.2 9.3 -9.3 11.3 -11.3
  variance 0.1 0.2 0.15 0.25 0.1 0.2 0.15 0.25 0.1 0.2 0.15 0.25
bG v mean 9 -9 11 -11 10 -10 12 -12 11 -11 13 -13
  variance 1 2 1.5 2.5 1 2 1.5 2.5 1 2 1.5 2.5
  1. 'gB', 'bB' and 'gG' stand for good, bad beta distributed data and good Gaussian distributed data re-spectively; 'bG m ' and 'bG v ' represent bad Gaussian distributed data which are hard to be clustered with respect to close means and large variances respectively.