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clustering evaluation framework
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fanny
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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=90
membexp=1.1
chang_pathbased
1.0
k=112
membexp=5.0
ppi_mips
1.0
k=159
membexp=5.0
chang_spiral
1.0
k=76
membexp=5.0
astral_40_strsim
1.0
k=260
membexp=5.0
astral_40_seqsim_beh
1.0
k=27
membexp=1.1
fraenti_s3
1.0
k=619
membexp=1.1
bone_marrow_fixLabels
0.982
k=7
membexp=2.0
fu_flame
1.0
k=111
membexp=5.0
coli_state
1.0
k=66
membexp=5.0
coli_find
1.0
k=161
membexp=1.1
coli_need
1.0
k=26
membexp=1.1
coli_time
1.0
k=181
membexp=2.0
gionis_aggregation
1.0
k=262
membexp=1.1
veenman_r15
1.0
k=237
membexp=5.0
zahn_compound
1.0
k=128
membexp=1.1
synthetic_spirals
1.0
k=94
membexp=2.0
synthetic_cassini
1.0
k=16
membexp=5.0
twonorm_100d
1.0
k=28
membexp=7.33
twonorm_50d
1.0
k=87
membexp=7.33
synthetic_cuboid
1.0
k=31
membexp=2.0
astral1_161
1.0
k=208
membexp=2.0
tcga
1.0
k=113
membexp=1.1
bone_marrow
0.993
k=16
membexp=1.1
zachary
1.0
k=2
membexp=2.0