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=253
samples=20
Clustering
Self Organizing Maps 0.0 x=185
y=132
Clustering
Spectral Clustering 0.0 k=118 Clustering
clusterdp 0.0 k=22
dc=2.587697389143054
Clustering
HDBSCAN 0.0 minPts=75
k=338
Clustering
AGNES 0.0 method=average
metric=euclidean
k=720
Clustering
c-Means 0.0 k=118
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=150 Clustering
DIANA 0.0 metric=euclidean
k=608
Clustering
DBSCAN 0.0 eps=7.763092167429162
MinPts=709
Clustering
Hierarchical Clustering 0.0 method=average
k=572
Clustering
fanny 0.0 k=271
membexp=5.0
Clustering
k-Means 0.0 k=742
nstart=10
Clustering
DensityCut 0.0 alpha=1.52587890625E-5
K=10
Clustering
clusterONE 1.0 s=473
d=0.0
Clustering
Affinity Propagation 0.0 dampfact=0.7
preference=38.815460837145814
maxits=2750
convits=275
Clustering
Markov Clustering 1.0 I=2.169069069069069 Clustering
Transitivity Clustering 0.0 T=38.58233494623203 Clustering
MCODE 0.0 v=0.9
cutoff=35.58083910071699
haircut=T
fluff=T
Clustering