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=15
samples=20
Clustering
Self Organizing Maps 0.946 x=2
y=17
Clustering
Spectral Clustering 1.0 k=11 Clustering
clusterdp 1.0 k=25
dc=0.130540204145823
Clustering
HDBSCAN 1.0 minPts=25
k=1
Clustering
AGNES 1.0 method=single
metric=euclidean
k=1
Clustering
c-Means 0.947 k=7
m=1.5
Clustering
k-Medoids (PAM) 0.944 k=3 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=0.0
MinPts=25
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.987 k=12
membexp=5.0
Clustering
k-Means 0.947 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.9523809523809523
K=12
Clustering
clusterONE 1.0 s=1
d=0.03333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=0.0
maxits=3500
convits=200
Clustering
Markov Clustering 1.0 I=1.1356356356356356 Clustering
Transitivity Clustering 1.0 T=0.007840252501250632 Clustering
MCODE 1.0 v=0.3
cutoff=1.3054020414582301
haircut=T
fluff=F
Clustering