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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
0.828
k=14
m=1.5
chang_pathbased
0.605
k=6
m=1.5
ppi_mips
0.8
k=531
m=1.01
chang_spiral
0.489
k=27
m=1.01
astral_40_strsim
0.843
k=193
m=2.25
astral_40_seqsim_beh
0.799
k=561
m=1.01
fraenti_s3
0.789
k=16
m=1.01
bone_marrow_fixLabels
0.581
k=4
m=1.5
fu_flame
0.611
k=3
m=1.5
coli_state
0.359
k=186
m=3.5
coli_find
0.546
k=6
m=1.5
coli_need
0.358
k=102
m=1.01
coli_time
0.397
k=140
m=1.5
gionis_aggregation
0.875
k=6
m=2.25
veenman_r15
0.994
k=20
m=1.5
zahn_compound
0.847
k=4
m=1.01
synthetic_spirals
0.3
k=52
m=1.5
synthetic_cassini
0.869
k=3
m=3.5
twonorm_100d
0.808
k=2
m=5.0
twonorm_50d
0.839
k=2
m=2.25
synthetic_cuboid
1.0
k=4
m=5.0
astral1_161
0.638
k=9
m=5.0
tcga
0.847
k=20
m=5.0
bone_marrow
0.735
k=3
m=1.5
zachary
1.0
k=8
m=2.25