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Table 2 KluDo’s performance over the test datasets

From: Assignment of structural domains in proteins using diffusion kernels on graphs

 

LED

MD

MED

RL

KK

SP

KK

SP

KK

SP

KK

SP

Benchmark_1

OL

89.9

91.2

90.1

90.3

89.7

90.9

88.6

89.7

ARI

92.5

93.8

92.5

92.5

92.2

93.8

92.0

92.5

Benchmark_2

OL

77.6

79.5

76.9

77.6

78.2

79.5

74.4

74.4

ARI

85.3

85.9

85.9

85.3

85.9

85.3

84.0

82.1

Benchmark_3

OL

80.7

83.7

80.7

82.2

81.5

83.7

77.0

79.3

ARI

87.4

88.9

87.4

88.9

87.4

88.1

85.2

85.2

Islam

OL

88.0

89.3

86.7

86.7

89.3

88.0

82.7

82.7

ARI

92.0

93.3

90.7

93.3

90.7

93.3

90.7

92.0

Jones

OL

94.5

92.7

89.1

92.7

90.9

92.7

90.9

92.7

ARI

96.4

98.2

96.4

98.2

94.5

98.2

98.2

98.2

ASTRAL40

OL

84.0

84.7

84.0

84.7

84.1

84.7

83.2

83.8

ARI

87.3

87.8

87.4

87.1

87.4

87.7

86.9

86.9

  1. KluDo’s accuracy for all combinations of the four kernels (LED, MD, MED and RL) and two clustering algorithms (kernel k-means and spectral clustering denoted by KK and SP, respectively) against the datasets Benchmark_1, Benchmark_2, Benchmark_3, Islam, Jones and ASTRAL40. The accuracies are based on the OL and ARI scores with the thresholds of 85% and 50%, respectively. The maximum accuracy in each row is illustrated in bold