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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
0.702
k=3
membexp=2.0
chang_pathbased
0.706
k=44
membexp=1.1
ppi_mips
0.907
k=528
membexp=1.1
chang_spiral
0.576
k=8
membexp=2.0
astral_40_strsim
0.617
k=219
membexp=2.0
astral_40_seqsim_beh
0.258
k=442
membexp=5.0
fraenti_s3
0.719
k=17
membexp=2.0
bone_marrow_fixLabels
0.944
k=2
membexp=2.0
fu_flame
0.776
k=3
membexp=2.0
coli_state
0.625
k=3
membexp=5.0
coli_find
0.356
k=4
membexp=5.0
coli_need
0.623
k=9
membexp=5.0
coli_time
0.513
k=11
membexp=1.1
gionis_aggregation
0.845
k=4
membexp=1.1
veenman_r15
0.982
k=18
membexp=2.0
zahn_compound
0.83
k=3
membexp=1.1
synthetic_spirals
0.706
k=2
membexp=5.0
synthetic_cassini
0.93
k=3
membexp=1.1
twonorm_100d
0.821
k=2
membexp=1.3966666666666667
twonorm_50d
0.951
k=2
membexp=1.99
synthetic_cuboid
1.0
k=6
membexp=1.1
astral1_161
0.413
k=46
membexp=2.0
tcga
0.926
k=2
membexp=5.0
bone_marrow
0.867
k=3
membexp=1.1
zachary
1.0
k=2
membexp=2.0