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=170
samples=20
Clustering
Self Organizing Maps 1.0 x=233
y=233
Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=18
dc=1.4104703215341545
Clustering
HDBSCAN 1.0 minPts=250
k=214
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=9
Clustering
c-Means 1.0 k=4
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=234 Clustering
DIANA 1.0 metric=euclidean
k=45
Clustering
DBSCAN 1.0 eps=1.5149496046107584
MinPts=183
Clustering
Hierarchical Clustering 1.0 method=average
k=157
Clustering
fanny 1.0 k=20
membexp=5.0
Clustering
k-Means 1.0 k=241
nstart=10
Clustering
DensityCut 1.0 alpha=0.05197704081632653
K=16
Clustering
clusterONE 0.0 s=200
d=0.23333333333333334
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=1.1753919346117954
maxits=5000
convits=275
Clustering
Markov Clustering 0.0 I=2.471971971971972 Clustering
Transitivity Clustering 1.0 T=1.094993086898943 Clustering
MCODE 1.0 v=0.2
cutoff=1.3059910384575504
haircut=T
fluff=F
Clustering