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 1.0 metric=euclidean
k=690
samples=20
Clustering
Self Organizing Maps 1.0 x=788
y=604
Clustering
Spectral Clustering 1.0 k=118 Clustering
clusterdp 1.0 k=11
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=53
k=683
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=624
Clustering
c-Means 1.0 k=4
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=670 Clustering
DIANA 1.0 metric=euclidean
k=570
Clustering
DBSCAN 1.0 eps=6.469243472857636
MinPts=683
Clustering
Hierarchical Clustering 1.0 method=complete
k=716
Clustering
fanny 1.0 k=271
membexp=5.0
Clustering
k-Means 1.0 k=363
nstart=10
Clustering
DensityCut 1.0 alpha=9.765625E-4
K=6
Clustering
clusterONE 0.0 s=263
d=0.3
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=38.815460837145814
maxits=5000
convits=350
Clustering
Markov Clustering 0.0 I=2.124524524524525 Clustering
Transitivity Clustering 1.0 T=36.095658776484946 Clustering
MCODE 1.0 v=0.8
cutoff=35.58083910071699
haircut=F
fluff=T
Clustering