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 0.706 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.496 x=2
y=1
Clustering
Spectral Clustering 0.706 k=2 Clustering
clusterdp 1.0 k=3
dc=2.5392
Clustering
HDBSCAN 1.0 minPts=6
k=2
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=2
Clustering
c-Means 0.496 k=2
m=2.25
Clustering
k-Medoids (PAM) 0.496 k=2 Clustering
DIANA 0.706 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=1.5456
MinPts=233
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.706 k=2
membexp=1.1
Clustering
k-Means 0.496 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.032552083333333315
K=5
Clustering
clusterONE 0.706 s=200
d=0.13333333333333333
Clustering
Affinity Propagation 0.706 dampfact=0.7
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering 0.706 I=3.567767767767768 Clustering
Transitivity Clustering 0.706 T=0.023207207207207207 Clustering
MCODE 0.622 v=0.2
cutoff=1.2420000000000002
haircut=T
fluff=T
Clustering