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 0.768 metric=euclidean
k=8
samples=20
Clustering
Self Organizing Maps 0.778 x=2
y=17
Clustering
Spectral Clustering 1.0 k=18 Clustering
clusterdp 1.0 k=4
dc=0.6527010207291151
Clustering
HDBSCAN 0.917 minPts=6
k=41
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=3
Clustering
c-Means 0.957 k=2
m=1.01
Clustering
k-Medoids (PAM) 0.95 k=2 Clustering
DIANA 0.772 metric=euclidean
k=2
Clustering
DBSCAN 1.0 eps=2.741344287062283
MinPts=191
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.957 k=3
membexp=1.1
Clustering
k-Means 0.78 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.94375
K=6
Clustering
clusterONE 0.524 s=216
d=0.26666666666666666
Clustering
Affinity Propagation 0.524 dampfact=0.9175
preference=0.0
maxits=3500
convits=200
Clustering
Markov Clustering 0.524 I=7.754954954954956 Clustering
Transitivity Clustering 0.885 T=2.6186443354177107 Clustering
MCODE 0.992 v=0.2
cutoff=1.6317525518227878
haircut=T
fluff=T
Clustering