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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
1.0
method=single
k=185
chang_pathbased
1.0
method=complete
k=265
ppi_mips
1.0
method=complete
k=1047
chang_spiral
1.0
method=complete
k=28
astral_40_strsim
1.0
method=complete
k=691
astral_40_seqsim_beh
1.0
method=single
k=1028
fraenti_s3
1.0
method=single
k=4954
bone_marrow_fixLabels
1.0
method=single
k=5
fu_flame
1.0
method=complete
k=206
coli_state
1.0
method=single
k=186
coli_find
1.0
method=average
k=420
coli_need
1.0
method=complete
k=101
coli_time
1.0
method=single
k=511
gionis_aggregation
1.0
method=average
k=383
veenman_r15
1.0
method=single
k=439
zahn_compound
1.0
method=average
k=183
synthetic_spirals
1.0
method=average
k=167
synthetic_cassini
1.0
method=complete
k=143
twonorm_100d
1.0
method=average
k=198
twonorm_50d
1.0
method=average
k=188
synthetic_cuboid
1.0
method=single
k=207
astral1_161
1.0
method=single
k=338
tcga
1.0
method=complete
k=49
bone_marrow
1.0
method=average
k=35
zachary
1.0
method=single
k=33