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clustering evaluation framework
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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
1.0
k=134
m=3.5
chang_pathbased
1.0
k=19
m=5.0
ppi_mips
1.0
k=421
m=5.0
chang_spiral
1.0
k=158
m=1.5
astral_40_strsim
1.0
k=252
m=5.0
astral_40_seqsim_beh
1.0
k=909
m=1.01
fraenti_s3
1.0
k=3586
m=1.01
bone_marrow_fixLabels
0.742
k=4
m=1.5
fu_flame
1.0
k=110
m=5.0
coli_state
1.0
k=24
m=2.25
coli_find
1.0
k=35
m=5.0
coli_need
1.0
k=100
m=5.0
coli_time
1.0
k=156
m=1.01
gionis_aggregation
1.0
k=121
m=1.01
veenman_r15
1.0
k=491
m=3.5
zahn_compound
1.0
k=38
m=1.5
synthetic_spirals
1.0
k=232
m=5.0
synthetic_cassini
1.0
k=109
m=5.0
twonorm_100d
1.0
k=32
m=2.25
twonorm_50d
1.0
k=46
m=2.25
synthetic_cuboid
1.0
k=132
m=3.5
astral1_161
1.0
k=109
m=1.5
tcga
1.0
k=60
m=1.01
bone_marrow
1.0
k=19
m=1.5
zachary
1.0
k=23
m=1.01