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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=9
chang_pathbased
0.994
method=complete
k=2
ppi_mips
1.0
method=average
k=123
chang_spiral
1.0
method=complete
k=3
astral_40_strsim
0.998
method=single
k=2
astral_40_seqsim_beh
0.998
method=average
k=2
fraenti_s3
1.0
method=average
k=2
bone_marrow_fixLabels
1.0
method=average
k=2
fu_flame
0.989
method=average
k=2
coli_state
0.995
method=single
k=2
coli_find
0.999
method=average
k=2
coli_need
0.975
method=average
k=3
coli_time
0.994
method=single
k=2
gionis_aggregation
1.0
method=complete
k=4
veenman_r15
1.0
method=average
k=3
zahn_compound
1.0
method=complete
k=2
synthetic_spirals
1.0
method=average
k=2
synthetic_cassini
1.0
method=average
k=2
twonorm_100d
0.99
method=average
k=3
twonorm_50d
0.99
method=single
k=2
synthetic_cuboid
1.0
method=average
k=3
astral1_161
0.996
method=complete
k=2
tcga
1.0
method=single
k=3
bone_marrow
0.973
method=complete
k=2
zachary
1.0
method=average
k=2