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clustering evaluation framework
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DIANA
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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
metric=euclidean
k=24
chang_pathbased
0.832
metric=euclidean
k=5
ppi_mips
0.989
metric=euclidean
k=978
chang_spiral
0.699
metric=euclidean
k=38
astral_40_strsim
0.991
metric=euclidean
k=462
astral_40_seqsim_beh
0.991
metric=euclidean
k=470
fraenti_s3
0.955
metric=euclidean
k=32
bone_marrow_fixLabels
0.9
metric=euclidean
k=20
fu_flame
0.75
metric=euclidean
k=1
coli_state
0.615
metric=euclidean
k=133
coli_find
0.874
metric=euclidean
k=399
coli_need
0.614
metric=euclidean
k=101
coli_time
0.736
metric=euclidean
k=479
gionis_aggregation
0.914
metric=euclidean
k=52
veenman_r15
0.995
metric=euclidean
k=12
zahn_compound
0.915
metric=euclidean
k=7
synthetic_spirals
0.515
metric=euclidean
k=64
synthetic_cassini
0.819
metric=euclidean
k=3
twonorm_100d
0.905
metric=euclidean
k=1
twonorm_50d
0.942
metric=euclidean
k=5
synthetic_cuboid
0.909
metric=euclidean
k=4
astral1_161
0.871
metric=euclidean
k=92
tcga
0.902
metric=euclidean
k=1
bone_marrow
0.875
metric=euclidean
k=3
zachary
0.806
metric=euclidean
k=9