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=264
samples=20
Clustering
Self Organizing Maps 1.0 x=385
y=239
Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 1.0 minPts=13
k=79
Clustering
AGNES 1.0 method=average
metric=euclidean
k=70
Clustering
c-Means 1.0 k=338
m=3.5
Clustering
k-Medoids (PAM) 1.0 k=177 Clustering
DIANA 1.0 metric=euclidean
k=374
Clustering
DBSCAN 1.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 1.0 method=complete
k=326
Clustering
fanny 1.0 k=161
membexp=2.0
Clustering
k-Means 1.0 k=285
nstart=10
Clustering
DensityCut 0.935 alpha=0.1853032879818594
K=3
Clustering
clusterONE 0.0 s=146
d=0.9
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=36.781585066443235
maxits=5000
convits=200
Clustering
Markov Clustering 0.0 I=2.3739739739739742 Clustering
Transitivity Clustering 1.0 T=36.781585066443235 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=F
Clustering