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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=26
m=1.01
chang_pathbased
1.0
k=233
m=3.5
ppi_mips
1.0
k=715
m=2.25
chang_spiral
1.0
k=305
m=1.01
astral_40_strsim
1.0
k=1049
m=1.5
astral_40_seqsim_beh
1.0
k=272
m=5.0
fraenti_s3
1.0
k=81
m=1.01
bone_marrow_fixLabels
0.742
k=3
m=3.5
fu_flame
1.0
k=69
m=1.5
coli_state
1.0
k=2
m=1.01
coli_find
1.0
k=67
m=2.25
coli_need
1.0
k=26
m=1.01
coli_time
1.0
k=16
m=1.5
gionis_aggregation
1.0
k=788
m=3.5
veenman_r15
1.0
k=352
m=2.25
zahn_compound
1.0
k=92
m=1.01
synthetic_spirals
1.0
k=97
m=3.5
synthetic_cassini
1.0
k=60
m=1.5
twonorm_100d
1.0
k=112
m=1.5
twonorm_50d
1.0
k=5
m=3.5
synthetic_cuboid
1.0
k=237
m=1.5
astral1_161
1.0
k=343
m=1.5
tcga
1.0
k=251
m=3.5
bone_marrow
1.0
k=35
m=5.0
zachary
1.0
k=34
m=3.5