Clust
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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=229
chang_pathbased
1.0
metric=euclidean
k=240
ppi_mips
1.0
metric=euclidean
k=1068
chang_spiral
1.0
metric=euclidean
k=162
astral_40_strsim
1.0
metric=euclidean
k=750
astral_40_seqsim_beh
1.0
metric=euclidean
k=900
fraenti_s3
1.0
metric=euclidean
k=4509
bone_marrow_fixLabels
0.984
metric=euclidean
k=20
fu_flame
1.0
metric=euclidean
k=167
coli_state
1.0
metric=euclidean
k=179
coli_find
1.0
metric=euclidean
k=418
coli_need
1.0
metric=euclidean
k=102
coli_time
1.0
metric=euclidean
k=509
gionis_aggregation
1.0
metric=euclidean
k=482
veenman_r15
1.0
metric=euclidean
k=485
zahn_compound
1.0
metric=euclidean
k=335
synthetic_spirals
1.0
metric=euclidean
k=80
synthetic_cassini
1.0
metric=euclidean
k=91
twonorm_100d
1.0
metric=euclidean
k=199
twonorm_50d
1.0
metric=euclidean
k=194
synthetic_cuboid
1.0
metric=euclidean
k=65
astral1_161
1.0
metric=euclidean
k=161
tcga
1.0
metric=euclidean
k=237
bone_marrow
1.0
metric=euclidean
k=35
zachary
1.0
metric=euclidean
k=12