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clustering evaluation framework
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k-Medoids (PAM)
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Best Parameters
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Best Qualities
Best Parameters
Hints:
Which parameter sets lead to the optimal clustering quality?
Please choose a clustering quality measure:
Davies Bouldin Index (R)
Dunn Index (R)
F1-Score
F2-Score
False Discovery Rate
False Positive Rate
Fowlkes Mallows Index (R)
Jaccard Index (R)
Rand Index
Rand Index (R)
Sensitivity
Silhouette Value (R)
Specificity
V-Measure
Dataset
Best quality
Parameter set
brown
0.999
k=7
chang_pathbased
0.798
k=2
ppi_mips
1.0
k=18
chang_spiral
0.5
k=13
astral_40_strsim
0.78
k=93
astral_40_seqsim_beh
0.988
k=2
fraenti_s3
0.938
k=2
bone_marrow_fixLabels
0.866
k=2
fu_flame
0.726
k=2
coli_state
0.54
k=5
coli_find
0.534
k=2
coli_need
0.951
k=2
coli_time
0.794
k=2
gionis_aggregation
0.995
k=2
veenman_r15
1.0
k=8
zahn_compound
1.0
k=3
synthetic_spirals
0.496
k=2
synthetic_cassini
0.944
k=3
twonorm_100d
0.547
k=3
twonorm_50d
0.77
k=4
synthetic_cuboid
1.0
k=4
astral1_161
0.6
k=5
tcga
0.97
k=2
bone_marrow
0.802
k=2
zachary
0.883
k=3