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 0.459 metric=euclidean
k=39
samples=20
Clustering
Self Organizing Maps 0.465 x=2
y=11
Clustering
Spectral Clustering 0.364 k=28 Clustering
clusterdp 0.236 k=2
dc=2.020516985548226
Clustering
HDBSCAN 0.044 minPts=31
k=259
Clustering
AGNES 0.455 method=average
metric=euclidean
k=38
Clustering
c-Means 0.478 k=37
m=2.25
Clustering
k-Medoids (PAM) 0.476 k=40 Clustering
DIANA 0.458 metric=euclidean
k=51
Clustering
DBSCAN 0.129 eps=5.051292463870565
MinPts=1
Clustering
Hierarchical Clustering 0.445 method=complete
k=48
Clustering
fanny 0.472 k=30
membexp=5.0
Clustering
k-Means 0.483 k=38
nstart=10
Clustering
DensityCut 0.187 alpha=0.12053571428571427
K=23
Clustering
clusterONE NaN s=52
d=0.26666666666666666
Clustering
Affinity Propagation 0.472 dampfact=0.7725
preference=22.73081608741754
maxits=3500
convits=200
Clustering
Markov Clustering NaN I=1.1712712712712714 Clustering
Transitivity Clustering 0.482 T=27.122255031232942 Clustering
MCODE 0.422 v=0.5
cutoff=26.519285435320466
haircut=T
fluff=F
Clustering