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=52
samples=20
Clustering
Self Organizing Maps 0.0 x=43
y=25
Clustering
Spectral Clustering 0.004 k=25 Clustering
clusterdp 0.0 k=8
dc=0.5223964153830202
Clustering
HDBSCAN 0.0 minPts=5
k=12
Clustering
AGNES 0.0 method=average
metric=euclidean
k=116
Clustering
c-Means 0.0 k=17
m=1.5
Clustering
k-Medoids (PAM) 0.0 k=130 Clustering
DIANA 0.0 metric=euclidean
k=52
Clustering
DBSCAN 0.0 eps=0.36567749076811407
MinPts=1
Clustering
Hierarchical Clustering 0.0 method=single
k=133
Clustering
fanny 0.0 k=250
membexp=2.0
Clustering
k-Means 0.0 k=56
nstart=10
Clustering
DensityCut 0.0 alpha=0.03968253968253968
K=35
Clustering
clusterONE 1.0 s=117
d=0.6666666666666666
Clustering
Affinity Propagation 0.0 dampfact=0.7725
preference=1.1753919346117954
maxits=5000
convits=350
Clustering
Markov Clustering 1.0 I=1.206906906906907 Clustering
Transitivity Clustering 0.0 T=1.0808742648615643 Clustering
MCODE 0.0 v=0.7
cutoff=1.2406914865346728
haircut=F
fluff=T
Clustering