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=780
samples=20
Clustering
Self Organizing Maps 1.0 x=630
y=263
Clustering
Spectral Clustering 1.0 k=118 Clustering
clusterdp 1.0 k=15
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=338
k=750
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=48
Clustering
c-Means 1.0 k=4
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=548 Clustering
DIANA 1.0 metric=euclidean
k=401
Clustering
DBSCAN 1.0 eps=0.0
MinPts=1
Clustering
Hierarchical Clustering 1.0 method=single
k=555
Clustering
fanny 1.0 k=271
membexp=5.0
Clustering
k-Means 1.0 k=435
nstart=10
Clustering
DensityCut 1.0 alpha=1.52587890625E-5
K=10
Clustering
clusterONE 0.0 s=578
d=0.5
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=38.815460837145814
maxits=4250
convits=350
Clustering
Markov Clustering 0.0 I=2.7837837837837838 Clustering
Transitivity Clustering 1.0 T=38.815460837145814 Clustering
MCODE 1.0 v=0.9
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering