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 0.0 metric=euclidean
k=192
samples=20
Clustering
Self Organizing Maps 0.0 x=293
y=213
Clustering
Spectral Clustering 0.014 k=24 Clustering
clusterdp 0.072 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 0.0 minPts=4
k=121
Clustering
AGNES 0.0 method=ward
metric=euclidean
k=150
Clustering
c-Means 0.0 k=378
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=199 Clustering
DIANA 0.0 metric=euclidean
k=172
Clustering
DBSCAN 0.0 eps=7.356317013288647
MinPts=332
Clustering
Hierarchical Clustering 0.0 method=average
k=283
Clustering
fanny 0.0 k=108
membexp=2.0
Clustering
k-Means 0.0 k=237
nstart=10
Clustering
DensityCut 0.192 alpha=0.1587301232512035
K=7
Clustering
clusterONE 0.753 s=399
d=0.8333333333333334
Clustering
Affinity Propagation 0.06 dampfact=0.9175
preference=27.586188799832428
maxits=4250
convits=425
Clustering
Markov Clustering 0.753 I=2.07997997997998 Clustering
Transitivity Clustering 0.0 T=36.19249061092463 Clustering
MCODE 0.043 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering