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=4
samples=20
Clustering
Self Organizing Maps 0.778 x=2
y=17
Clustering
Spectral Clustering 1.0 k=18 Clustering
clusterdp 1.0 k=6
dc=0.522160816583292
Clustering
HDBSCAN 0.917 minPts=6
k=41
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=3
Clustering
c-Means 0.957 k=3
m=3.5
Clustering
k-Medoids (PAM) 0.95 k=2 Clustering
DIANA 0.772 metric=euclidean
k=3
Clustering
DBSCAN 1.0 eps=2.8718844912081063
MinPts=183
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=0.95625
K=6
Clustering
clusterONE 0.524 s=1
d=0.1
Clustering
Affinity Propagation 0.524 dampfact=0.7
preference=0.0
maxits=3500
convits=500
Clustering
Markov Clustering 0.524 I=1.58998998998999 Clustering
Transitivity Clustering 0.885 T=2.6186443354177107 Clustering
MCODE 0.992 v=0.2
cutoff=1.6317525518227878
haircut=F
fluff=T
Clustering