Clusteval logo ClustEval clustering evaluation framework

Which parameter sets lead to the optimal clustering quality?

Please choose a clustering quality measure:
Program Best quality Parameter set Clustering
CLARA 0.5 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.408 x=2
y=1
Clustering
Spectral Clustering 0.93 k=3 Clustering
clusterdp 1.0 k=3
dc=4.041033971096452
Clustering
HDBSCAN 0.947 minPts=5
k=34
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=3
Clustering
c-Means 0.441 k=11
m=1.01
Clustering
k-Medoids (PAM) 0.418 k=9 Clustering
DIANA 0.5 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=21.21542834825637
MinPts=260
Clustering
Hierarchical Clustering 1.0 method=complete
k=3
Clustering
fanny 0.5 k=3
membexp=5.0
Clustering
k-Means 0.463 k=7
nstart=10
Clustering
DensityCut 1.0 alpha=0.03968253968253968
K=7
Clustering
clusterONE 0.5 s=52
d=0.7666666666666667
Clustering
Affinity Propagation 0.5 dampfact=0.9175
preference=0.0
maxits=3500
convits=500
Clustering
Markov Clustering 0.5 I=1.5365365365365364 Clustering
Transitivity Clustering 0.5 T=1.4865665509288748 Clustering
MCODE 0.484 v=0.3
cutoff=17.679523623546974
haircut=F
fluff=T
Clustering