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=361
samples=20
Clustering
Self Organizing Maps 0.0 x=399
y=359
Clustering
Spectral Clustering 0.014 k=24 Clustering
clusterdp 0.072 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 0.0 minPts=8
k=168
Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=277
Clustering
c-Means 0.0 k=378
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=319 Clustering
DIANA 0.0 metric=euclidean
k=372
Clustering
DBSCAN 0.0 eps=4.904211342192431
MinPts=359
Clustering
Hierarchical Clustering 0.0 method=average
k=312
Clustering
fanny 0.0 k=129
membexp=1.1
Clustering
k-Means 0.0 k=201
nstart=10
Clustering
DensityCut 0.192 alpha=0.169890873015873
K=7
Clustering
clusterONE 0.753 s=186
d=0.36666666666666664
Clustering
Affinity Propagation 0.06 dampfact=0.7725
preference=27.586188799832428
maxits=2750
convits=425
Clustering
Markov Clustering 0.753 I=3.0955955955955954 Clustering
Transitivity Clustering 0.0 T=35.566577751936094 Clustering
MCODE 0.043 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering