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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
0.78
k=14
m=1.5
chang_pathbased
0.707
k=6
m=2.25
ppi_mips
0.306
k=695
m=2.25
chang_spiral
0.441
k=11
m=1.01
astral_40_strsim
0.677
k=107
m=5.0
astral_40_seqsim_beh
0.524
k=194
m=1.01
fraenti_s3
0.847
k=16
m=1.01
bone_marrow_fixLabels
0.791
k=4
m=1.5
fu_flame
0.857
k=2
m=1.01
coli_state
0.478
k=2
m=1.01
coli_find
0.231
k=4
m=1.5
coli_need
0.536
k=3
m=5.0
coli_time
0.398
k=4
m=1.01
gionis_aggregation
0.893
k=4
m=2.25
veenman_r15
0.997
k=20
m=1.5
zahn_compound
0.833
k=4
m=1.01
synthetic_spirals
0.504
k=3
m=5.0
synthetic_cassini
0.957
k=3
m=3.5
twonorm_100d
0.97
k=3
m=1.5
twonorm_50d
0.977
k=2
m=2.25
synthetic_cuboid
1.0
k=4
m=1.01
astral1_161
0.663
k=9
m=5.0
tcga
0.963
k=20
m=5.0
bone_marrow
0.921
k=3
m=2.25
zachary
1.0
k=2
m=1.01