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=13
m=1.01
chang_pathbased
1.0
k=236
m=3.5
ppi_mips
1.0
k=244
m=2.25
chang_spiral
1.0
k=130
m=2.25
astral_40_strsim
1.0
k=996
m=3.5
astral_40_seqsim_beh
1.0
k=156
m=1.5
fraenti_s3
1.0
k=929
m=2.25
bone_marrow_fixLabels
0.742
k=4
m=1.5
fu_flame
1.0
k=220
m=3.5
coli_state
1.0
k=24
m=2.25
coli_find
1.0
k=35
m=5.0
coli_need
1.0
k=100
m=5.0
coli_time
1.0
k=156
m=1.01
gionis_aggregation
1.0
k=205
m=5.0
veenman_r15
1.0
k=491
m=3.5
zahn_compound
1.0
k=83
m=1.5
synthetic_spirals
1.0
k=15
m=1.01
synthetic_cassini
1.0
k=50
m=5.0
twonorm_100d
1.0
k=32
m=2.25
twonorm_50d
1.0
k=46
m=2.25
synthetic_cuboid
1.0
k=132
m=3.5
astral1_161
1.0
k=109
m=1.5
tcga
1.0
k=184
m=3.5
bone_marrow
1.0
k=10
m=5.0
zachary
1.0
k=2
m=2.25