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=533
samples=20
Clustering
Self Organizing Maps 0.0 x=600
y=600
Clustering
Spectral Clustering 0.0 k=100 Clustering
clusterdp 0.0 k=25
dc=0.9295510122873538
Clustering
HDBSCAN 0.0 minPts=58
k=457
Clustering
AGNES 0.0 method=complete
metric=euclidean
k=592
Clustering
c-Means 0.0 k=255
m=1.01
Clustering
k-Medoids (PAM) 0.0 k=424 Clustering
DIANA 0.0 metric=euclidean
k=473
Clustering
DBSCAN 0.0 eps=0.4647755061436769
MinPts=280
Clustering
Hierarchical Clustering 0.0 method=complete
k=486
Clustering
fanny 0.0 k=99
membexp=5.0
Clustering
k-Means 0.0 k=577
nstart=10
Clustering
DensityCut 0.0 alpha=0.3872023809523809
K=23
Clustering
clusterONE 1.0 s=200
d=0.7666666666666667
Clustering
Affinity Propagation 0.0 dampfact=0.845
preference=13.943265184310308
maxits=4250
convits=350
Clustering
Markov Clustering 1.0 I=5.046646646646647 Clustering
Transitivity Clustering 0.0 T=13.88743629468344 Clustering
MCODE 0.001 v=0.9
cutoff=13.36229580163071
haircut=T
fluff=F
Clustering