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 0.0 metric=euclidean
k=276
samples=20
Clustering
Self Organizing Maps 0.0 x=293
y=213
Clustering
Spectral Clustering 0.001 k=24 Clustering
clusterdp 0.009 k=3
dc=4.904211342192431
Clustering
HDBSCAN 0.0 minPts=57
k=380
Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=345
Clustering
c-Means 0.0 k=78
m=1.01
Clustering
k-Medoids (PAM) 0.0 k=227 Clustering
DIANA 0.0 metric=euclidean
k=213
Clustering
DBSCAN 0.0 eps=12.260528355481078
MinPts=306
Clustering
Hierarchical Clustering 0.0 method=complete
k=220
Clustering
fanny 0.0 k=86
membexp=5.0
Clustering
k-Means 0.0 k=120
nstart=10
Clustering
DensityCut 0.065 alpha=0.1427862811791383
K=3
Clustering
clusterONE 1.0 s=372
d=0.26666666666666666
Clustering
Affinity Propagation 0.0 dampfact=0.99
preference=36.781585066443235
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=1.4207207207207209 Clustering
Transitivity Clustering 0.0 T=35.713851365815756 Clustering
MCODE 0.004 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering