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=3
dc=1.5456
Clustering
HDBSCAN 1.0 minPts=6
k=2
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=2
Clustering
c-Means 0.504 k=2
m=1.01
Clustering
k-Medoids (PAM) 0.504 k=2 Clustering
DIANA 0.667 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=2.3184
MinPts=200
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.667 k=10
membexp=5.0
Clustering
k-Means 0.502 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.1125
K=3
Clustering
clusterONE 0.667 s=1
d=0.5333333333333333
Clustering
Affinity Propagation 0.667 dampfact=0.9175
preference=0.0
maxits=4250
convits=500
Clustering
Markov Clustering 0.667 I=2.0621621621621626 Clustering
Transitivity Clustering 0.667 T=0.09945945945945947 Clustering
MCODE 0.637 v=0.3
cutoff=1.2420000000000002
haircut=F
fluff=F
Clustering