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 1.0 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.496 x=2
y=1
Clustering
Spectral Clustering 0.854 k=3 Clustering
clusterdp 1.0 k=25
dc=28.28723779767516
Clustering
HDBSCAN 1.0 minPts=134
k=1
Clustering
AGNES 1.0 method=average
metric=euclidean
k=5
Clustering
c-Means 0.496 k=2
m=1.01
Clustering
k-Medoids (PAM) 0.5 k=13 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=23.235945333804597
MinPts=115
Clustering
Hierarchical Clustering 1.0 method=complete
k=3
Clustering
fanny 1.0 k=11
membexp=1.1
Clustering
k-Means 0.496 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.05952380553242706
K=6
Clustering
clusterONE 1.0 s=125
d=0.23333333333333334
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=22.73081608741754
maxits=4250
convits=200
Clustering
Markov Clustering 1.0 I=3.6568568568568574 Clustering
Transitivity Clustering 1.0 T=11.771180035926601 Clustering
MCODE 0.496 v=0.7
cutoff=17.679523623546974
haircut=F
fluff=F
Clustering