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 0.573 metric=euclidean
k=29
samples=20
Clustering
Self Organizing Maps 0.12 x=2
y=1
Clustering
Spectral Clustering 0.578 k=42 Clustering
clusterdp 0.594 k=17
dc=36008.34461886732
Clustering
HDBSCAN 0.272 minPts=92
k=2054
Clustering
AGNES 0.537 method=ward
metric=euclidean
k=11
Clustering
c-Means 0.576 k=16
m=1.01
Clustering
k-Medoids (PAM) 0.57 k=17 Clustering
DIANA 0.474 metric=euclidean
k=16
Clustering
DBSCAN 0.334 eps=504116.8246641425
MinPts=3833
Clustering
Hierarchical Clustering 0.481 method=complete
k=17
Clustering
fanny 0.561 k=17
membexp=2.0
Clustering
k-Means 0.577 k=47
nstart=10
Clustering
DensityCut 0.606 alpha=0.9877929687500002
K=121
Clustering
clusterONE 0.067 s=500
d=0.36666666666666664
Clustering
Affinity Propagation 0.231 dampfact=0.7725
preference=0.0
maxits=2000
convits=350
Clustering
Markov Clustering 0.067 I=4.645745745745746 Clustering
Transitivity Clustering 0.548 T=967791.8448614491 Clustering
MCODE 0.112 v=0.0
cutoff=990229.4770188513
haircut=T
fluff=F
Clustering