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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=complete
k=149
chang_pathbased
1.0
method=average
k=45
ppi_mips
1.0
method=average
k=1094
chang_spiral
1.0
method=single
k=262
astral_40_strsim
1.0
method=complete
k=849
astral_40_seqsim_beh
1.0
method=average
k=1002
fraenti_s3
1.0
method=complete
k=4969
bone_marrow_fixLabels
1.0
method=average
k=5
fu_flame
1.0
method=single
k=176
coli_state
1.0
method=average
k=168
coli_find
1.0
method=average
k=418
coli_need
1.0
method=average
k=105
coli_time
1.0
method=complete
k=511
gionis_aggregation
1.0
method=average
k=513
veenman_r15
1.0
method=complete
k=589
zahn_compound
1.0
method=average
k=90
synthetic_spirals
1.0
method=average
k=69
synthetic_cassini
1.0
method=single
k=229
twonorm_100d
1.0
method=complete
k=200
twonorm_50d
1.0
method=complete
k=197
synthetic_cuboid
1.0
method=average
k=241
astral1_161
1.0
method=complete
k=231
tcga
1.0
method=average
k=127
bone_marrow
1.0
method=single
k=33
zachary
1.0
method=single
k=24