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=580
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=143
k=257
Clustering
AGNES 0.0 method=flexible
metric=euclidean
k=483
Clustering
c-Means 0.0 k=551
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=450 Clustering
DIANA 0.0 metric=euclidean
k=553
Clustering
DBSCAN 0.0 eps=0.4647755061436769
MinPts=280
Clustering
Hierarchical Clustering 0.0 method=average
k=567
Clustering
fanny 0.0 k=268
membexp=2.0
Clustering
k-Means 0.0 k=563
nstart=10
Clustering
DensityCut 0.007 alpha=0.3720238095238095
K=26
Clustering
clusterONE 0.935 s=20
d=0.6
Clustering
Affinity Propagation 0.004 dampfact=0.99
preference=10.457448888232731
maxits=4250
convits=350
Clustering
Markov Clustering 0.935 I=7.746046046046047 Clustering
Transitivity Clustering 0.0 T=13.929307961903591 Clustering
MCODE 0.017 v=0.4
cutoff=12.781326418951116
haircut=T
fluff=F
Clustering