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=84
samples=20
Clustering
Self Organizing Maps 0.0 x=293
y=213
Clustering
Spectral Clustering 0.014 k=24 Clustering
clusterdp 0.072 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 0.0 minPts=22
k=112
Clustering
AGNES 0.0 method=average
metric=euclidean
k=156
Clustering
c-Means 0.0 k=286
m=2.25
Clustering
k-Medoids (PAM) 0.0 k=398 Clustering
DIANA 0.0 metric=euclidean
k=285
Clustering
DBSCAN 0.0 eps=6.130264177740539
MinPts=332
Clustering
Hierarchical Clustering 0.0 method=single
k=324
Clustering
fanny 0.0 k=102
membexp=1.1
Clustering
k-Means 0.0 k=269
nstart=10
Clustering
DensityCut 0.192 alpha=0.1773313492063492
K=8
Clustering
clusterONE 0.753 s=253
d=0.6666666666666666
Clustering
Affinity Propagation 0.06 dampfact=0.845
preference=27.586188799832428
maxits=3500
convits=275
Clustering
Markov Clustering 0.753 I=4.574474474474474 Clustering
Transitivity Clustering 0.0 T=35.566577751936094 Clustering
MCODE 0.043 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering