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=592
samples=20
Clustering
Self Organizing Maps 0.0 x=185
y=132
Clustering
Spectral Clustering 0.0 k=118 Clustering
clusterdp 0.0 k=23
dc=2.587697389143054
Clustering
HDBSCAN 0.0 minPts=150
k=638
Clustering
AGNES 0.0 method=single
metric=euclidean
k=208
Clustering
c-Means 0.0 k=533
m=2.25
Clustering
k-Medoids (PAM) 0.0 k=144 Clustering
DIANA 0.0 metric=euclidean
k=586
Clustering
DBSCAN 0.0 eps=6.469243472857636
MinPts=683
Clustering
Hierarchical Clustering 0.0 method=average
k=662
Clustering
fanny 0.0 k=229
membexp=1.1
Clustering
k-Means 0.0 k=760
nstart=10
Clustering
DensityCut 0.0 alpha=1.52587890625E-5
K=10
Clustering
clusterONE 1.0 s=1
d=1.0
Clustering
Affinity Propagation 0.0 dampfact=0.9175
preference=38.815460837145814
maxits=2000
convits=425
Clustering
Markov Clustering 1.0 I=9.305105105105104 Clustering
Transitivity Clustering 0.0 T=37.99952021894755 Clustering
MCODE 0.0 v=0.8
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering