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=533
samples=20
Clustering
Self Organizing Maps 1.0 x=101
y=40
Clustering
Spectral Clustering 1.0 k=100 Clustering
clusterdp 1.0 k=22
dc=0.9295510122873538
Clustering
HDBSCAN 1.0 minPts=18
k=118
Clustering
AGNES 1.0 method=average
metric=euclidean
k=563
Clustering
c-Means 1.0 k=195
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=591 Clustering
DIANA 1.0 metric=euclidean
k=505
Clustering
DBSCAN 1.0 eps=0.0
MinPts=1
Clustering
Hierarchical Clustering 1.0 method=average
k=536
Clustering
fanny 1.0 k=279
membexp=2.0
Clustering
k-Means 1.0 k=596
nstart=10
Clustering
DensityCut 1.0 alpha=0.3720238095238095
K=21
Clustering
clusterONE 0.0 s=400
d=0.1
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=13.943265184310308
maxits=2750
convits=275
Clustering
Markov Clustering 0.0 I=1.616716716716717 Clustering
Transitivity Clustering 1.0 T=13.859521849870005 Clustering
MCODE 0.999 v=0.8
cutoff=13.36229580163071
haircut=T
fluff=T
Clustering