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=281
samples=20
Clustering
Self Organizing Maps 1.0 x=788
y=604
Clustering
Spectral Clustering 1.0 k=118 Clustering
clusterdp 1.0 k=17
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=188
k=563
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=77
Clustering
c-Means 1.0 k=438
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=40 Clustering
DIANA 1.0 metric=euclidean
k=581
Clustering
DBSCAN 1.0 eps=3.881546083714581
MinPts=630
Clustering
Hierarchical Clustering 1.0 method=single
k=255
Clustering
fanny 1.0 k=196
membexp=5.0
Clustering
k-Means 1.0 k=429
nstart=10
Clustering
DensityCut 1.0 alpha=0.00234375
K=13
Clustering
clusterONE 0.0 s=27
d=0.8333333333333334
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=38.815460837145814
maxits=2750
convits=425
Clustering
Markov Clustering 0.0 I=1.6612612612612614 Clustering
Transitivity Clustering 1.0 T=37.80524864318606 Clustering
MCODE 1.0 v=0.9
cutoff=35.58083910071699
haircut=T
fluff=F
Clustering