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=359
samples=20
Clustering
Self Organizing Maps 1.0 x=385
y=239
Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=3
dc=4.904211342192431
Clustering
HDBSCAN 1.0 minPts=1
k=304
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=83
Clustering
c-Means 1.0 k=29
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=242 Clustering
DIANA 1.0 metric=euclidean
k=347
Clustering
DBSCAN 1.0 eps=11.034475519932972
MinPts=253
Clustering
Hierarchical Clustering 1.0 method=complete
k=309
Clustering
fanny 1.0 k=178
membexp=1.1
Clustering
k-Means 1.0 k=233
nstart=10
Clustering
DensityCut 0.935 alpha=0.12152777777777776
K=3
Clustering
clusterONE 0.0 s=67
d=0.9333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=36.781585066443235
maxits=5000
convits=500
Clustering
Markov Clustering 0.0 I=9.438738738738738 Clustering
Transitivity Clustering 1.0 T=35.566577751936094 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=F
Clustering