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=2
samples=20
Clustering
Self Organizing Maps 1.0 x=2
y=1
Clustering
Spectral Clustering 1.0 k=6 Clustering
clusterdp 1.0 k=2
dc=12.260528355481078
Clustering
HDBSCAN 1.0 minPts=173
k=1
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=1
Clustering
c-Means 1.0 k=2
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=3 Clustering
DIANA 1.0 metric=euclidean
k=30
Clustering
DBSCAN 1.0 eps=13.486581191029186
MinPts=372
Clustering
Hierarchical Clustering 1.0 method=single
k=2
Clustering
fanny 1.0 k=2
membexp=1.1
Clustering
k-Means 1.0 k=5
nstart=10
Clustering
DensityCut 1.0 alpha=0.5238095238095238
K=95
Clustering
clusterONE 1.0 s=14
d=0.0
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.0
maxits=2750
convits=275
Clustering
Markov Clustering 1.0 I=1.7503503503503506 Clustering
Transitivity Clustering 1.0 T=10.787792216684553 Clustering
MCODE 0.816 v=0.9
cutoff=29.11875484426756
haircut=T
fluff=T
Clustering