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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
0.79
k=101
membexp=5.0
chang_pathbased
0.597
k=16
membexp=2.0
ppi_mips
0.968
k=528
membexp=1.1
chang_spiral
0.503
k=26
membexp=5.0
astral_40_strsim
0.879
k=219
membexp=2.0
astral_40_seqsim_beh
0.791
k=505
membexp=5.0
fraenti_s3
0.784
k=17
membexp=2.0
bone_marrow_fixLabels
0.906
k=2
membexp=2.0
fu_flame
0.607
k=4
membexp=5.0
coli_state
0.356
k=65
membexp=1.1
coli_find
0.546
k=57
membexp=5.0
coli_need
0.356
k=30
membexp=5.0
coli_time
0.397
k=196
membexp=2.0
gionis_aggregation
0.88
k=4
membexp=5.0
veenman_r15
0.988
k=18
membexp=2.0
zahn_compound
0.806
k=3
membexp=1.1
synthetic_spirals
0.274
k=84
membexp=5.0
synthetic_cassini
0.884
k=3
membexp=5.0
twonorm_100d
0.606
k=21
membexp=8.516666666666667
twonorm_50d
0.838
k=2
membexp=1.99
synthetic_cuboid
1.0
k=4
membexp=1.1
astral1_161
0.537
k=50
membexp=1.1
tcga
0.828
k=2
membexp=5.0
bone_marrow
0.735
k=3
membexp=1.1
zachary
1.0
k=2
membexp=2.0