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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
0.759
k=21
membexp=2.0
chang_pathbased
0.754
k=7
membexp=2.0
ppi_mips
0.925
k=528
membexp=1.1
chang_spiral
0.714
k=7
membexp=1.1
astral_40_strsim
0.657
k=172
membexp=5.0
astral_40_seqsim_beh
0.399
k=253
membexp=1.1
fraenti_s3
0.827
k=17
membexp=2.0
bone_marrow_fixLabels
0.973
k=2
membexp=2.0
fu_flame
0.865
k=3
membexp=2.0
coli_state
0.698
k=3
membexp=5.0
coli_find
0.399
k=32
membexp=5.0
coli_need
0.739
k=6
membexp=1.1
coli_time
0.597
k=14
membexp=2.0
gionis_aggregation
0.887
k=4
membexp=5.0
veenman_r15
0.986
k=18
membexp=2.0
zahn_compound
0.875
k=3
membexp=1.1
synthetic_spirals
0.833
k=2
membexp=2.0
synthetic_cassini
0.956
k=3
membexp=1.1
twonorm_100d
0.898
k=2
membexp=1.3966666666666667
twonorm_50d
0.975
k=2
membexp=1.99
synthetic_cuboid
1.0
k=5
membexp=1.1
astral1_161
0.555
k=62
membexp=5.0
tcga
0.943
k=2
membexp=5.0
bone_marrow
0.921
k=3
membexp=1.1
zachary
1.0
k=2
membexp=2.0