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=281
samples=20
Clustering
Self Organizing Maps 0.0 x=385
y=239
Clustering
Spectral Clustering 0.014 k=24 Clustering
clusterdp 0.072 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 0.0 minPts=37
k=121
Clustering
AGNES 0.0 method=ward
metric=euclidean
k=136
Clustering
c-Means 0.0 k=399
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=274 Clustering
DIANA 0.0 metric=euclidean
k=392
Clustering
DBSCAN 0.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 0.0 method=single
k=364
Clustering
fanny 0.0 k=102
membexp=1.1
Clustering
k-Means 0.0 k=275
nstart=10
Clustering
DensityCut 0.192 alpha=0.12152777777777776
K=12
Clustering
clusterONE 0.753 s=253
d=0.0
Clustering
Affinity Propagation 0.06 dampfact=0.845
preference=27.586188799832428
maxits=3500
convits=350
Clustering
Markov Clustering 0.753 I=9.091291291291292 Clustering
Transitivity Clustering 0.0 T=35.640214558875925 Clustering
MCODE 0.043 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=T
Clustering