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=8
samples=20
Clustering
Self Organizing Maps 0.987 x=2
y=1
Clustering
Spectral Clustering 1.0 k=15 Clustering
clusterdp 1.0 k=3
dc=13.47848967816663
Clustering
HDBSCAN 1.0 minPts=29
k=1
Clustering
AGNES 1.0 method=average
metric=euclidean
k=14
Clustering
c-Means 1.0 k=2
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=2 Clustering
DIANA 1.0 metric=euclidean
k=12
Clustering
DBSCAN 1.0 eps=3.7182040491494153
MinPts=440
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=9
membexp=5.0
Clustering
k-Means 1.0 k=5
nstart=10
Clustering
DensityCut 1.0 alpha=0.3642857142857142
K=95
Clustering
clusterONE 1.0 s=200
d=0.03333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering 1.0 I=10.0 Clustering
Transitivity Clustering 1.0 T=9.602569015821313 Clustering
MCODE 0.909 v=0.5
cutoff=12.781326418951116
haircut=F
fluff=F
Clustering