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
0.798
k=4
m=2.25
chang_pathbased
0.686
k=6
m=1.01
ppi_mips
0.141
k=2
m=3.5
chang_spiral
0.404
k=7
m=2.25
astral_40_strsim
0.48
k=193
m=2.25
astral_40_seqsim_beh
0.256
k=425
m=3.5
fraenti_s3
0.731
k=16
m=1.01
bone_marrow_fixLabels
0.767
k=4
m=1.5
fu_flame
0.759
k=2
m=1.5
coli_state
0.491
k=2
m=1.5
coli_find
0.332
k=2
m=1.5
coli_need
0.573
k=2
m=1.01
coli_time
0.492
k=2
m=2.25
gionis_aggregation
0.871
k=8
m=3.5
veenman_r15
0.993
k=20
m=1.5
zahn_compound
0.854
k=4
m=1.01
synthetic_spirals
0.496
k=2
m=3.5
synthetic_cassini
0.93
k=2
m=5.0
twonorm_100d
0.941
k=2
m=5.0
twonorm_50d
0.955
k=2
m=2.25
synthetic_cuboid
1.0
k=4
m=1.01
astral1_161
0.587
k=9
m=5.0
tcga
0.959
k=20
m=5.0
bone_marrow
0.867
k=3
m=2.25
zachary
1.0
k=4
m=5.0