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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
0.987
method=complete
k=25
chang_pathbased
0.736
method=single
k=11
ppi_mips
0.88
method=average
k=153
chang_spiral
1.0
method=complete
k=3
astral_40_strsim
0.849
method=complete
k=184
astral_40_seqsim_beh
0.714
method=single
k=278
fraenti_s3
0.79
method=complete
k=17
bone_marrow_fixLabels
1.0
method=complete
k=4
fu_flame
0.972
method=single
k=12
coli_state
0.546
method=single
k=7
coli_find
0.237
method=complete
k=73
coli_need
0.547
method=average
k=3
coli_time
0.404
method=complete
k=9
gionis_aggregation
0.967
method=single
k=6
veenman_r15
0.994
method=complete
k=14
zahn_compound
0.874
method=single
k=57
synthetic_spirals
1.0
method=average
k=2
synthetic_cassini
1.0
method=average
k=2
twonorm_100d
0.89
method=single
k=2
twonorm_50d
0.897
method=average
k=12
synthetic_cuboid
1.0
method=average
k=5
astral1_161
0.747
method=complete
k=52
tcga
0.998
method=average
k=4
bone_marrow
0.933
method=average
k=4
zachary
1.0
method=average
k=2