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 1.0 metric=euclidean
k=83
samples=20
Clustering
Self Organizing Maps 1.0 x=385
y=239
Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 1.0 minPts=38
k=285
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=127
Clustering
c-Means 1.0 k=102
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=242 Clustering
DIANA 1.0 metric=euclidean
k=164
Clustering
DBSCAN 1.0 eps=7.356317013288647
MinPts=332
Clustering
Hierarchical Clustering 1.0 method=single
k=329
Clustering
fanny 1.0 k=137
membexp=1.1
Clustering
k-Means 1.0 k=386
nstart=10
Clustering
DensityCut 0.935 alpha=0.12152777777777776
K=3
Clustering
clusterONE 0.0 s=54
d=0.7666666666666667
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=36.781585066443235
maxits=3500
convits=350
Clustering
Markov Clustering 0.0 I=5.8662662662662655 Clustering
Transitivity Clustering 1.0 T=36.781585066443235 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering