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=243
samples=20
Clustering
Self Organizing Maps 1.0 x=2
y=11
Clustering
Spectral Clustering 1.0 k=33 Clustering
clusterdp 1.0 k=5
dc=12.123101913289355
Clustering
HDBSCAN 1.0 minPts=4
k=33
Clustering
AGNES 1.0 method=average
metric=euclidean
k=239
Clustering
c-Means 1.0 k=243
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=84 Clustering
DIANA 1.0 metric=euclidean
k=231
Clustering
DBSCAN 1.0 eps=28.28723779767516
MinPts=239
Clustering
Hierarchical Clustering 1.0 method=average
k=137
Clustering
fanny 1.0 k=129
membexp=2.0
Clustering
k-Means 1.0 k=306
nstart=10
Clustering
DensityCut 1.0 alpha=0.06696428571428571
K=3
Clustering
clusterONE 0.0 s=156
d=0.43333333333333335
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=30.307754783223388
maxits=4250
convits=275
Clustering
Markov Clustering 0.0 I=1.4296296296296296 Clustering
Transitivity Clustering 1.0 T=26.54583126658705 Clustering
MCODE 1.0 v=0.2
cutoff=26.519285435320466
haircut=F
fluff=T
Clustering