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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=101
m=1.01
chang_pathbased
1.0
k=50
m=1.5
ppi_mips
1.0
k=962
m=1.5
chang_spiral
1.0
k=149
m=2.25
astral_40_strsim
1.0
k=162
m=3.5
astral_40_seqsim_beh
1.0
k=262
m=5.0
fraenti_s3
1.0
k=93
m=1.5
bone_marrow_fixLabels
0.742
k=4
m=1.5
fu_flame
1.0
k=211
m=5.0
coli_state
1.0
k=24
m=2.25
coli_find
1.0
k=420
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=205
m=5.0
veenman_r15
1.0
k=491
m=3.5
zahn_compound
1.0
k=38
m=1.5
synthetic_spirals
1.0
k=189
m=1.5
synthetic_cassini
1.0
k=214
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=181
m=1.5
bone_marrow
1.0
k=11
m=2.25
zachary
1.0
k=4
m=2.25