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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.
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BMC Bioinformatics

ISSN: 1471-2105

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