Skip to main content

Table 1 Classification error and computation time for various clustering methods applied to simulated data.

From: Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

Classification Error (%)

 

J

HC

DynTree

HOPACH (best)

HOPACH (greedy)

MM(1–6)

RPMM (ICL-BIC)

RPMM (BIC)

Case 1

25

33.2

44.7

9.9

16.4

12.6

15.5

15.4

 

50

32.5

43.8

5.0

10.0

6.2

5.5

5.5

 

500

33.9

38.4

3.5

11.3

1.5

0.1

0.1

 

1000

34.0

38.5

9.2

14.4

1.1

0.1

0.1

Case 2

5

59.4

60.5

65.1

65.8

59.4

59.4

59.4

 

10

58.9

60.0

66.9

67.5

59.2

59.2

59.2

 

25

30.0

39.6

4.1

8.1

0.0

0.0

0.0

 

50

29.9

39.6

3.6

6.4

0.3

0.3

0.3

Computation Time (seconds)

 

J

HC

DynTree

HOPACH (best)

HOPACH (greedy)

MM(1–6)

RPMM (ICL-BIC)

RPMM (BIC)

Case 1

25

0.00

0.04

4.15

1.18

36.39

13.80

13.83

 

50

0.01

0.05

3.29

1.09

51.14

14.23

14.23

 

500

0.03

0.08

2.98

1.04

436.82

90.99

91.05

 

1000

0.06

0.11

3.05

1.10

848.10

176.99

176.81

Case 2

5

0.00

0.04

2.80

1.21

29.73

5.14

6.09

 

10

0.00

0.04

2.01

1.13

46.48

9.69

10.05

 

25

0.00

0.01

3.33

1.23

34.56

8.85

8.86

 

50

0.01

0.01

2.63

1.16

47.52

10.90

10.86

  1. HC = Hierarchical clustering
  2. DynTree = Hierarchical clustering with classes determined by dynamic tree cutting
  3. HOPACH(best) = HOPACH with 'best' number of classes
  4. HOPACH(greedy) = HOPACH with 'greedy' number of classes
  5. MM(1–6) = Beta mixture model fitting 1–6 classes sequentially
  6. RPMM (ICL-BIC) = Recursively partitioned mixture model employing ICL-BIC
  7. RPMM (BIC) = Recursively partitioned mixture model employing BIC
  8. J = Number of loci considered in analysis
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us