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 0.0 metric=euclidean
k=590
samples=20
Clustering
Self Organizing Maps 0.0 x=600
y=600
Clustering
Spectral Clustering 0.0 k=100 Clustering
clusterdp 0.0 k=24
dc=0.9295510122873538
Clustering
HDBSCAN 0.0 minPts=6
k=155
Clustering
AGNES 0.0 method=single
metric=euclidean
k=461
Clustering
c-Means 0.0 k=358
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=553 Clustering
DIANA 0.0 metric=euclidean
k=567
Clustering
DBSCAN 0.0 eps=0.9295510122873538
MinPts=120
Clustering
Hierarchical Clustering 0.0 method=single
k=430
Clustering
fanny 0.0 k=144
membexp=1.1
Clustering
k-Means 0.0 k=509
nstart=10
Clustering
DensityCut 0.0 alpha=0.36532738095238093
K=27
Clustering
clusterONE 1.0 s=180
d=0.3333333333333333
Clustering
Affinity Propagation 0.0 dampfact=0.9175
preference=13.943265184310308
maxits=4250
convits=200
Clustering
Markov Clustering 1.0 I=6.872972972972973 Clustering
Transitivity Clustering 0.0 T=13.873479072276723 Clustering
MCODE 0.001 v=0.9
cutoff=13.36229580163071
haircut=T
fluff=F
Clustering