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=258
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=11
k=86
Clustering
AGNES 0.0 method=average
metric=euclidean
k=238
Clustering
c-Means 0.0 k=44
m=3.5
Clustering
k-Medoids (PAM) 0.0 k=327 Clustering
DIANA 0.0 metric=euclidean
k=278
Clustering
DBSCAN 0.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 0.0 method=complete
k=355
Clustering
fanny 0.0 k=107
membexp=1.1
Clustering
k-Means 0.0 k=275
nstart=10
Clustering
DensityCut 0.065 alpha=0.15341553287981857
K=3
Clustering
clusterONE 1.0 s=226
d=1.0
Clustering
Affinity Propagation 0.0 dampfact=0.99
preference=36.781585066443235
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=9.162562562562563 Clustering
Transitivity Clustering 0.0 T=35.566577751936094 Clustering
MCODE 0.004 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=T
Clustering