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 0.667 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.504 x=2
y=1
Clustering
Spectral Clustering 0.698 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 0.504 k=2
m=1.5
Clustering
k-Medoids (PAM) 0.504 k=2 Clustering
DIANA 0.667 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=1.5456
MinPts=233
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.667 k=2
membexp=1.1
Clustering
k-Means 0.502 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.05468749999999998
K=4
Clustering
clusterONE 0.667 s=216
d=0.03333333333333333
Clustering
Affinity Propagation 0.667 dampfact=0.9175
preference=1.6560000000000001
maxits=3500
convits=500
Clustering
Markov Clustering 0.667 I=6.757157157157157 Clustering
Transitivity Clustering 0.667 T=0.22212612612612614 Clustering
MCODE 0.637 v=0.2
cutoff=1.2420000000000002
haircut=T
fluff=T
Clustering