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=512
samples=20
Clustering
Self Organizing Maps 1.0 x=600
y=600
Clustering
Spectral Clustering 1.0 k=100 Clustering
clusterdp 1.0 k=25
dc=0.9295510122873538
Clustering
HDBSCAN 1.0 minPts=229
k=600
Clustering
AGNES 1.0 method=single
metric=euclidean
k=585
Clustering
c-Means 1.0 k=545
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=585 Clustering
DIANA 1.0 metric=euclidean
k=553
Clustering
DBSCAN 1.0 eps=0.0
MinPts=1
Clustering
Hierarchical Clustering 1.0 method=average
k=583
Clustering
fanny 1.0 k=277
membexp=2.0
Clustering
k-Means 1.0 k=498
nstart=10
Clustering
DensityCut 1.0 alpha=0.1976190476190476
K=24
Clustering
clusterONE 0.0 s=460
d=0.1
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=13.943265184310308
maxits=3500
convits=350
Clustering
Markov Clustering 0.0 I=2.480880880880881 Clustering
Transitivity Clustering 1.0 T=13.943265184310308 Clustering
MCODE 0.999 v=0.8
cutoff=13.36229580163071
haircut=T
fluff=T
Clustering