Clust
Eval
clustering evaluation framework
Welcome
Overview
Clustering Methods
Data Sets
Measures
Submit
Advanced
Help
About us
Location:
Clustering Methods
»
Hierarchical Clustering
»
Best Parameters
Navigation:
General
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=219
chang_pathbased
1.0
method=average
k=272
ppi_mips
1.0
method=single
k=1041
chang_spiral
1.0
method=single
k=210
astral_40_strsim
1.0
method=average
k=956
astral_40_seqsim_beh
1.0
method=complete
k=644
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=average
k=542
zahn_compound
1.0
method=average
k=138
synthetic_spirals
1.0
method=average
k=187
synthetic_cassini
1.0
method=single
k=239
twonorm_100d
1.0
method=average
k=198
twonorm_50d
1.0
method=complete
k=186
synthetic_cuboid
1.0
method=complete
k=193
astral1_161
1.0
method=complete
k=385
tcga
1.0
method=average
k=78
bone_marrow
1.0
method=average
k=32
zachary
1.0
method=complete
k=25