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=55
samples=20
Clustering
Self Organizing Maps 0.931 x=2
y=1
Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=7
dc=1.5149496046107584
Clustering
HDBSCAN 1.0 minPts=16
k=1
Clustering
AGNES 1.0 method=single
metric=euclidean
k=2
Clustering
c-Means 1.0 k=4
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=0.7835946230745302
MinPts=117
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=14
membexp=1.1
Clustering
k-Means 1.0 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.9523809523809523
K=226
Clustering
clusterONE 1.0 s=84
d=0.36666666666666664
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.7835946230745302
maxits=2750
convits=500
Clustering
Markov Clustering 1.0 I=5.082282282282283 Clustering
Transitivity Clustering 1.0 T=0.594559283574068 Clustering
MCODE 0.852 v=0.6
cutoff=0.5223964153830202
haircut=T
fluff=T
Clustering