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=3
samples=20
Clustering
Self Organizing Maps 0.931 x=2
y=1
Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=4
dc=0.20895856615320804
Clustering
HDBSCAN 1.0 minPts=7
k=1
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=3
Clustering
c-Means 1.0 k=3
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=0.9403135476894363
MinPts=125
Clustering
Hierarchical Clustering 1.0 method=average
k=5
Clustering
fanny 1.0 k=2
membexp=1.1
Clustering
k-Means 1.0 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.7619047619047619
K=226
Clustering
clusterONE 1.0 s=108
d=0.26666666666666666
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.3917973115372651
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=5.42082082082082 Clustering
Transitivity Clustering 1.0 T=0.11765685031149102 Clustering
MCODE 0.852 v=0.3
cutoff=0.5223964153830202
haircut=T
fluff=F
Clustering