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=138
samples=20
Clustering
Self Organizing Maps 1.0 x=33
y=8
Clustering
Spectral Clustering 0.998 k=34 Clustering
clusterdp 1.0 k=13
dc=4.919490714381814
Clustering
HDBSCAN 1.0 minPts=3
k=55
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=13
Clustering
c-Means 1.0 k=170
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=215 Clustering
DIANA 1.0 metric=euclidean
k=123
Clustering
DBSCAN 1.0 eps=0.0
MinPts=40
Clustering
Hierarchical Clustering 1.0 method=complete
k=213
Clustering
fanny 1.0 k=90
membexp=2.0
Clustering
k-Means 1.0 k=109
nstart=10
Clustering
DensityCut 1.0 alpha=0.5089285714285714
K=11
Clustering
clusterONE 0.0 s=48
d=0.36666666666666664
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=14.758472143145443
maxits=5000
convits=500
Clustering
Markov Clustering 0.0 I=1.9908908908908909 Clustering
Transitivity Clustering 1.0 T=12.926589714967228 Clustering
MCODE 0.991 v=0.3
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering