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 0.0 metric=euclidean
k=459
samples=20
Clustering
Self Organizing Maps 0.0 x=600
y=600
Clustering
Spectral Clustering 0.013 k=39 Clustering
clusterdp 0.003 k=17
dc=0.9295510122873538
Clustering
HDBSCAN 0.0 minPts=24
k=161
Clustering
AGNES 0.0 method=ward
metric=euclidean
k=556
Clustering
c-Means 0.0 k=551
m=2.25
Clustering
k-Medoids (PAM) 0.0 k=532 Clustering
DIANA 0.0 metric=euclidean
k=469
Clustering
DBSCAN 0.0 eps=0.9295510122873538
MinPts=500
Clustering
Hierarchical Clustering 0.0 method=average
k=558
Clustering
fanny 0.0 k=270
membexp=1.1
Clustering
k-Means 0.0 k=555
nstart=10
Clustering
DensityCut 0.007 alpha=0.3872023809523809
K=20
Clustering
clusterONE 0.935 s=400
d=0.5333333333333333
Clustering
Affinity Propagation 0.004 dampfact=0.7
preference=10.457448888232731
maxits=2750
convits=350
Clustering
Markov Clustering 0.935 I=9.83073073073073 Clustering
Transitivity Clustering 0.0 T=13.929307961903591 Clustering
MCODE 0.017 v=0.5
cutoff=12.781326418951116
haircut=F
fluff=F
Clustering