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=377
samples=20
Clustering
Self Organizing Maps 0.0 x=293
y=213
Clustering
Spectral Clustering 0.001 k=24 Clustering
clusterdp 0.009 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 0.0 minPts=190
k=361
Clustering
AGNES 0.0 method=single
metric=euclidean
k=321
Clustering
c-Means 0.0 k=322
m=1.01
Clustering
k-Medoids (PAM) 0.0 k=203 Clustering
DIANA 0.0 metric=euclidean
k=215
Clustering
DBSCAN 0.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 0.0 method=average
k=143
Clustering
fanny 0.0 k=91
membexp=5.0
Clustering
k-Means 0.0 k=397
nstart=10
Clustering
DensityCut 0.065 alpha=0.13215702947845803
K=3
Clustering
clusterONE 1.0 s=332
d=0.26666666666666666
Clustering
Affinity Propagation 0.0 dampfact=0.7725
preference=36.781585066443235
maxits=3500
convits=200
Clustering
Markov Clustering 1.0 I=3.077777777777778 Clustering
Transitivity Clustering 0.0 T=35.60339615540602 Clustering
MCODE 0.004 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=T
Clustering