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=449
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=200
k=571
Clustering
AGNES 0.0 method=complete
metric=euclidean
k=592
Clustering
c-Means 0.0 k=473
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=506 Clustering
DIANA 0.0 metric=euclidean
k=544
Clustering
DBSCAN 0.0 eps=0.9295510122873538
MinPts=120
Clustering
Hierarchical Clustering 0.0 method=average
k=574
Clustering
fanny 0.0 k=273
membexp=2.0
Clustering
k-Means 0.0 k=539
nstart=10
Clustering
DensityCut 0.007 alpha=0.4809523809523809
K=22
Clustering
clusterONE 0.935 s=580
d=0.6666666666666666
Clustering
Affinity Propagation 0.004 dampfact=0.845
preference=10.457448888232731
maxits=4250
convits=425
Clustering
Markov Clustering 0.935 I=9.25165165165165 Clustering
Transitivity Clustering 0.0 T=13.915350739496873 Clustering
MCODE 0.017 v=0.5
cutoff=12.781326418951116
haircut=T
fluff=T
Clustering