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=12 Clustering
clusterdp 1.0 k=7
dc=1.3943265184310307
Clustering
HDBSCAN 1.0 minPts=400
k=1
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=8 Clustering
DIANA 1.0 metric=euclidean
k=57
Clustering
DBSCAN 1.0 eps=5.112530567580446
MinPts=160
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=14
membexp=5.0
Clustering
k-Means 1.0 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.5642857142857143
K=142
Clustering
clusterONE 1.0 s=240
d=0.8
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=0.0
maxits=3500
convits=500
Clustering
Markov Clustering 1.0 I=3.8973973973973974 Clustering
Transitivity Clustering 1.0 T=10.495831249851202 Clustering
MCODE 0.909 v=0.9
cutoff=12.781326418951116
haircut=F
fluff=F
Clustering