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=649
samples=20
Clustering
Self Organizing Maps 1.0 x=788
y=735
Clustering
Spectral Clustering 1.0 k=118 Clustering
clusterdp 1.0 k=8
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=38
k=413
Clustering
AGNES 1.0 method=single
metric=euclidean
k=22
Clustering
c-Means 1.0 k=176
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=150 Clustering
DIANA 1.0 metric=euclidean
k=594
Clustering
DBSCAN 1.0 eps=6.469243472857636
MinPts=683
Clustering
Hierarchical Clustering 1.0 method=average
k=546
Clustering
fanny 1.0 k=238
membexp=2.0
Clustering
k-Means 1.0 k=338
nstart=10
Clustering
DensityCut 1.0 alpha=9.765625E-4
K=6
Clustering
clusterONE 0.0 s=368
d=0.13333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=29.11159562785936
maxits=5000
convits=500
Clustering
Markov Clustering 0.0 I=2.124524524524525 Clustering
Transitivity Clustering 1.0 T=35.823678570418856 Clustering
MCODE 1.0 v=0.8
cutoff=35.58083910071699
haircut=F
fluff=T
Clustering