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clustering evaluation framework
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Hierarchical Clustering
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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
0.999
method=complete
k=25
chang_pathbased
0.605
method=average
k=9
ppi_mips
0.811
method=average
k=153
chang_spiral
1.0
method=complete
k=3
astral_40_strsim
0.69
method=complete
k=228
astral_40_seqsim_beh
0.418
method=single
k=278
fraenti_s3
0.481
method=complete
k=17
bone_marrow_fixLabels
1.0
method=complete
k=4
fu_flame
0.918
method=single
k=12
coli_state
0.405
method=single
k=7
coli_find
0.13
method=single
k=15
coli_need
0.381
method=single
k=4
coli_time
0.263
method=average
k=2
gionis_aggregation
0.973
method=single
k=6
veenman_r15
0.977
method=complete
k=14
zahn_compound
0.92
method=single
k=57
synthetic_spirals
1.0
method=average
k=2
synthetic_cassini
1.0
method=average
k=2
twonorm_100d
0.671
method=single
k=2
twonorm_50d
0.677
method=average
k=12
synthetic_cuboid
1.0
method=complete
k=3
astral1_161
0.508
method=complete
k=44
tcga
0.991
method=average
k=4
bone_marrow
0.782
method=average
k=4
zachary
1.0
method=average
k=2