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=140
samples=20
Clustering
Self Organizing Maps 1.0 x=73
y=208
Clustering
Spectral Clustering 0.998 k=34 Clustering
clusterdp 1.0 k=16
dc=9.838981428763628
Clustering
HDBSCAN 1.0 minPts=1
k=232
Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=228
Clustering
c-Means 1.0 k=45
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=156 Clustering
DIANA 1.0 metric=euclidean
k=206
Clustering
DBSCAN 1.0 eps=8.855083285887266
MinPts=176
Clustering
Hierarchical Clustering 1.0 method=complete
k=135
Clustering
fanny 1.0 k=100
membexp=1.1
Clustering
k-Means 1.0 k=109
nstart=10
Clustering
DensityCut 1.0 alpha=0.5733816964285714
K=12
Clustering
clusterONE 0.0 s=192
d=0.3333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=14.758472143145443
maxits=3500
convits=500
Clustering
Markov Clustering 0.0 I=9.073473473473474 Clustering
Transitivity Clustering 1.0 T=12.926589714967228 Clustering
MCODE 0.991 v=0.3
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering