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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
0.899
k=14
m=1.5
chang_pathbased
0.823
k=6
m=1.01
ppi_mips
0.984
k=796
m=2.25
chang_spiral
0.705
k=26
m=2.25
astral_40_strsim
0.991
k=213
m=5.0
astral_40_seqsim_beh
0.99
k=561
m=1.01
fraenti_s3
0.965
k=16
m=1.01
bone_marrow_fixLabels
0.795
k=3
m=3.5
fu_flame
0.759
k=3
m=1.5
coli_state
0.616
k=43
m=1.5
coli_find
0.873
k=398
m=2.25
coli_need
0.614
k=81
m=5.0
coli_time
0.736
k=505
m=3.5
gionis_aggregation
0.934
k=8
m=3.5
veenman_r15
0.999
k=20
m=1.5
zahn_compound
0.91
k=6
m=1.5
synthetic_spirals
0.52
k=44
m=5.0
synthetic_cassini
0.951
k=2
m=5.0
twonorm_100d
0.942
k=30
m=1.01
twonorm_50d
0.956
k=2
m=2.25
synthetic_cuboid
1.0
k=4
m=2.25
astral1_161
0.872
k=23
m=5.0
tcga
0.953
k=20
m=5.0
bone_marrow
0.9
k=3
m=5.0
zachary
1.0
k=3
m=1.01