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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
0.994
method=complete
k=25
chang_pathbased
0.7
method=average
k=9
ppi_mips
0.963
method=average
k=153
chang_spiral
1.0
method=complete
k=3
astral_40_strsim
0.947
method=complete
k=184
astral_40_seqsim_beh
0.881
method=average
k=345
fraenti_s3
0.75
method=complete
k=17
bone_marrow_fixLabels
1.0
method=complete
k=4
fu_flame
0.822
method=single
k=12
coli_state
0.383
method=single
k=129
coli_find
0.546
method=average
k=418
coli_need
0.359
method=single
k=103
coli_time
0.397
method=complete
k=509
gionis_aggregation
0.971
method=single
k=6
veenman_r15
0.991
method=complete
k=14
zahn_compound
0.843
method=complete
k=6
synthetic_spirals
1.0
method=average
k=2
synthetic_cassini
1.0
method=average
k=2
twonorm_100d
0.509
method=single
k=2
twonorm_50d
0.524
method=average
k=12
synthetic_cuboid
1.0
method=complete
k=3
astral1_161
0.733
method=complete
k=52
tcga
0.986
method=average
k=4
bone_marrow
0.789
method=average
k=4
zachary
1.0
method=average
k=2