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=165
samples=20
Clustering
Self Organizing Maps 0.0 x=185
y=132
Clustering
Spectral Clustering 0.006 k=62 Clustering
clusterdp 0.0 k=8
dc=2.587697389143054
Clustering
HDBSCAN 0.0 minPts=79
k=446
Clustering
AGNES 0.0 method=flexible
metric=euclidean
k=279
Clustering
c-Means 0.0 k=780
m=3.5
Clustering
k-Medoids (PAM) 0.0 k=586 Clustering
DIANA 0.0 metric=euclidean
k=401
Clustering
DBSCAN 0.0 eps=5.175394778286108
MinPts=604
Clustering
Hierarchical Clustering 0.0 method=average
k=643
Clustering
fanny 0.0 k=200
membexp=2.0
Clustering
k-Means 0.0 k=266
nstart=10
Clustering
DensityCut 0.0 alpha=7.8125E-4
K=10
Clustering
clusterONE 0.783 s=132
d=0.5333333333333333
Clustering
Affinity Propagation 0.002 dampfact=0.99
preference=9.703865209286453
maxits=4250
convits=425
Clustering
Markov Clustering 0.783 I=3.8528528528528527 Clustering
Transitivity Clustering 0.0 T=34.81346637645911 Clustering
MCODE 0.001 v=0.8
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering