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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
0.796
metric=euclidean
k=31
chang_pathbased
0.515
metric=euclidean
k=3
ppi_mips
0.028
metric=euclidean
k=897
chang_spiral
0.458
metric=euclidean
k=51
astral_40_strsim
0.128
metric=euclidean
k=501
astral_40_seqsim_beh
0.104
metric=euclidean
k=516
fraenti_s3
0.429
metric=euclidean
k=7
bone_marrow_fixLabels
0.15
metric=euclidean
k=20
fu_flame
0.434
metric=euclidean
k=4
coli_state
0.782
metric=euclidean
k=5
coli_find
0.769
metric=euclidean
k=119
coli_need
0.78
metric=euclidean
k=1
coli_time
0.841
metric=euclidean
k=4
gionis_aggregation
0.522
metric=euclidean
k=3
veenman_r15
0.703
metric=euclidean
k=16
zahn_compound
0.638
metric=euclidean
k=5
synthetic_spirals
0.354
metric=euclidean
k=86
synthetic_cassini
0.508
metric=euclidean
k=2
twonorm_100d
0.038
metric=euclidean
k=12
twonorm_50d
0.075
metric=euclidean
k=4
synthetic_cuboid
0.534
metric=euclidean
k=7
astral1_161
0.104
metric=euclidean
k=223
tcga
0.184
metric=euclidean
k=2
bone_marrow
0.312
metric=euclidean
k=2
zachary
0.214
metric=euclidean
k=22