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=2
samples=20
Clustering
Self Organizing Maps 0.931 x=2
y=1
Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=24
dc=0.2611982076915101
Clustering
HDBSCAN 1.0 minPts=10
k=1
Clustering
AGNES 1.0 method=average
metric=euclidean
k=3
Clustering
c-Means 1.0 k=4
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=65
Clustering
DBSCAN 1.0 eps=0.9925531892277383
MinPts=42
Clustering
Hierarchical Clustering 1.0 method=average
k=5
Clustering
fanny 1.0 k=2
membexp=1.1
Clustering
k-Means 1.0 k=5
nstart=10
Clustering
DensityCut 1.0 alpha=0.19841269841269843
K=15
Clustering
clusterONE 1.0 s=84
d=0.36666666666666664
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.3917973115372651
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=4.2003003003003005 Clustering
Transitivity Clustering 1.0 T=0.23531370062298204 Clustering
MCODE 0.852 v=0.3
cutoff=0.5223964153830202
haircut=T
fluff=T
Clustering