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 0.521 metric=euclidean
k=38
samples=20
Clustering
Self Organizing Maps 0.515 x=2
y=25
Clustering
Spectral Clustering 0.75 k=2 Clustering
clusterdp 1.0 k=5
dc=1.3248
Clustering
HDBSCAN 1.0 minPts=6
k=2
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=2
Clustering
c-Means 0.52 k=44
m=5.0
Clustering
k-Medoids (PAM) 0.52 k=37 Clustering
DIANA 0.515 metric=euclidean
k=54
Clustering
DBSCAN 1.0 eps=1.7664000000000002
MinPts=191
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.515 k=22
membexp=1.1
Clustering
k-Means 0.518 k=12
nstart=10
Clustering
DensityCut 1.0 alpha=0.04045758928571427
K=5
Clustering
clusterONE 0.498 s=117
d=0.13333333333333333
Clustering
Affinity Propagation 0.521 dampfact=0.9175
preference=2.484
maxits=4250
convits=200
Clustering
Markov Clustering 0.498 I=8.387487487487489 Clustering
Transitivity Clustering 0.527 T=2.924108108108108 Clustering
MCODE 0.525 v=0.1
cutoff=1.518
haircut=F
fluff=T
Clustering