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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
0.953
k=11
chang_pathbased
0.66
k=3
ppi_mips
0.833
k=130
chang_spiral
0.406
k=13
astral_40_strsim
0.824
k=112
astral_40_seqsim_beh
0.46
k=463
fraenti_s3
0.727
k=17
bone_marrow_fixLabels
0.896
k=5
fu_flame
0.753
k=2
coli_state
0.466
k=5
coli_find
0.267
k=2
coli_need
0.605
k=3
coli_time
0.452
k=2
gionis_aggregation
0.838
k=4
veenman_r15
0.993
k=15
zahn_compound
0.83
k=3
synthetic_spirals
0.496
k=2
synthetic_cassini
0.912
k=2
twonorm_100d
0.547
k=3
twonorm_50d
0.76
k=4
synthetic_cuboid
1.0
k=4
astral1_161
0.509
k=6
tcga
0.909
k=2
bone_marrow
0.839
k=4
zachary
0.892
k=7