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clustering evaluation framework
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k-Medoids (PAM)
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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
0.938
k=37
chang_pathbased
0.582
k=13
ppi_mips
0.952
k=130
chang_spiral
0.492
k=36
astral_40_strsim
0.94
k=112
astral_40_seqsim_beh
0.853
k=463
fraenti_s3
0.786
k=17
bone_marrow_fixLabels
0.849
k=4
fu_flame
0.558
k=4
coli_state
0.371
k=161
coli_find
0.546
k=414
coli_need
0.359
k=101
coli_time
0.397
k=510
gionis_aggregation
0.888
k=7
veenman_r15
0.994
k=15
zahn_compound
0.806
k=3
synthetic_spirals
0.305
k=45
synthetic_cassini
0.833
k=2
twonorm_100d
0.232
k=198
twonorm_50d
0.442
k=4
synthetic_cuboid
1.0
k=4
astral1_161
0.549
k=68
tcga
0.701
k=2
bone_marrow
0.714
k=4
zachary
0.831
k=7