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=1
samples=20
Clustering
Self Organizing Maps 0.987 x=2
y=1
Clustering
Spectral Clustering 1.0 k=13 Clustering
clusterdp 1.0 k=25
dc=10.689836641304568
Clustering
HDBSCAN 1.0 minPts=34
k=6
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=4
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=9
Clustering
DBSCAN 1.0 eps=2.7886530368620615
MinPts=360
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=16
membexp=5.0
Clustering
k-Means 1.0 k=5
nstart=10
Clustering
DensityCut 1.0 alpha=0.49761904761904757
K=95
Clustering
clusterONE 1.0 s=160
d=0.8
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering 1.0 I=4.1913913913913925 Clustering
Transitivity Clustering 1.0 T=0.0 Clustering
MCODE 0.909 v=0.5
cutoff=12.781326418951116
haircut=F
fluff=F
Clustering