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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
0.562
k=14
m=1.5
chang_pathbased
0.542
k=3
m=1.01
ppi_mips
0.012
k=13
m=5.0
chang_spiral
0.478
k=37
m=2.25
astral_40_strsim
0.075
k=107
m=5.0
astral_40_seqsim_beh
0.058
k=455
m=1.01
fraenti_s3
0.481
k=16
m=1.01
bone_marrow_fixLabels
0.26
k=4
m=1.5
fu_flame
0.443
k=2
m=3.5
coli_state
0.483
k=2
m=1.01
coli_find
0.636
k=2
m=1.5
coli_need
0.651
k=2
m=1.5
coli_time
0.728
k=2
m=1.5
gionis_aggregation
0.524
k=4
m=1.5
veenman_r15
0.753
k=20
m=1.5
zahn_compound
0.638
k=2
m=2.25
synthetic_spirals
0.397
k=52
m=1.5
synthetic_cassini
0.51
k=7
m=1.5
twonorm_100d
0.069
k=20
m=2.25
twonorm_50d
0.091
k=13
m=5.0
synthetic_cuboid
0.563
k=6
m=5.0
astral1_161
0.066
k=98
m=2.25
tcga
0.18
k=3
m=2.25
bone_marrow
0.352
k=3
m=1.5
zachary
0.156
k=11
m=5.0