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=451
samples=20
Clustering
Self Organizing Maps 1.0 x=101
y=40
Clustering
Spectral Clustering 1.0 k=100 Clustering
clusterdp 1.0 k=19
dc=0.9295510122873538
Clustering
HDBSCAN 1.0 minPts=600
k=143
Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=512
Clustering
c-Means 1.0 k=55
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=586 Clustering
DIANA 1.0 metric=euclidean
k=508
Clustering
DBSCAN 1.0 eps=0.9295510122873538
MinPts=500
Clustering
Hierarchical Clustering 1.0 method=single
k=484
Clustering
fanny 1.0 k=180
membexp=2.0
Clustering
k-Means 1.0 k=597
nstart=10
Clustering
DensityCut 1.0 alpha=0.3559523809523809
K=22
Clustering
clusterONE 0.0 s=240
d=0.7333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=13.943265184310308
maxits=2000
convits=200
Clustering
Markov Clustering 0.0 I=1.6790790790790793 Clustering
Transitivity Clustering 1.0 T=13.859521849870005 Clustering
MCODE 0.999 v=0.8
cutoff=13.36229580163071
haircut=T
fluff=T
Clustering