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 0.651 metric=euclidean
k=2
samples=20
Clustering
Self Organizing Maps 0.653 x=2
y=17
Clustering
Spectral Clustering 1.0 k=18 Clustering
clusterdp 1.0 k=4
dc=0.6527010207291151
Clustering
HDBSCAN 0.702 minPts=6
k=41
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=3
Clustering
c-Means 0.869 k=2
m=1.01
Clustering
k-Medoids (PAM) 0.839 k=2 Clustering
DIANA 0.651 metric=euclidean
k=2
Clustering
DBSCAN 1.0 eps=2.741344287062283
MinPts=191
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.869 k=3
membexp=1.1
Clustering
k-Means 0.653 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.94375
K=6
Clustering
clusterONE 0.357 s=1
d=0.03333333333333333
Clustering
Affinity Propagation 0.357 dampfact=0.7725
preference=0.0
maxits=4250
convits=200
Clustering
Markov Clustering 0.357 I=1.4741741741741743 Clustering
Transitivity Clustering 0.657 T=1.8228587065407718 Clustering
MCODE 0.974 v=0.2
cutoff=1.6317525518227878
haircut=T
fluff=T
Clustering