Clust
Eval
clustering evaluation framework
Welcome
Overview
Clustering Methods
Data Sets
Measures
Submit
Advanced
Help
About us
Location:
Clustering Methods
»
c-Means
»
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
k=211
m=3.5
chang_pathbased
1.0
k=212
m=2.25
ppi_mips
1.0
k=715
m=2.25
chang_spiral
1.0
k=305
m=1.01
astral_40_strsim
1.0
k=162
m=2.25
astral_40_seqsim_beh
1.0
k=124
m=1.01
fraenti_s3
1.0
k=456
m=5.0
bone_marrow_fixLabels
0.742
k=4
m=1.5
fu_flame
1.0
k=172
m=2.25
coli_state
1.0
k=23
m=2.25
coli_find
1.0
k=67
m=2.25
coli_need
1.0
k=26
m=1.01
coli_time
1.0
k=16
m=1.5
gionis_aggregation
1.0
k=788
m=3.5
veenman_r15
1.0
k=352
m=2.25
zahn_compound
1.0
k=92
m=1.01
synthetic_spirals
1.0
k=43
m=1.5
synthetic_cassini
1.0
k=24
m=3.5
twonorm_100d
1.0
k=112
m=1.5
twonorm_50d
1.0
k=80
m=5.0
synthetic_cuboid
1.0
k=4
m=3.5
astral1_161
1.0
k=496
m=1.5
tcga
1.0
k=43
m=1.01
bone_marrow
1.0
k=37
m=1.5
zachary
1.0
k=3
m=5.0