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=134
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=380
k=361
Clustering
AGNES 0.0 method=average
metric=euclidean
k=88
Clustering
c-Means 0.0 k=346
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=151 Clustering
DIANA 0.0 metric=euclidean
k=251
Clustering
DBSCAN 0.0 eps=12.260528355481078
MinPts=306
Clustering
Hierarchical Clustering 0.0 method=single
k=174
Clustering
fanny 0.0 k=78
membexp=5.0
Clustering
k-Means 0.0 k=129
nstart=10
Clustering
DensityCut 0.192 alpha=0.16338045634920634
K=7
Clustering
clusterONE 0.753 s=27
d=0.8666666666666667
Clustering
Affinity Propagation 0.06 dampfact=0.845
preference=27.586188799832428
maxits=3500
convits=425
Clustering
Markov Clustering 0.753 I=3.1757757757757763 Clustering
Transitivity Clustering 0.0 T=36.04521699704497 Clustering
MCODE 0.043 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=F
Clustering