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=342
samples=20
Clustering
Self Organizing Maps 1.0 x=293
y=213
Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=24
dc=3.6781585066443236
Clustering
HDBSCAN 1.0 minPts=4
k=100
Clustering
AGNES 1.0 method=single
metric=euclidean
k=394
Clustering
c-Means 1.0 k=399
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=263 Clustering
DIANA 1.0 metric=euclidean
k=391
Clustering
DBSCAN 1.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 1.0 method=single
k=290
Clustering
fanny 1.0 k=115
membexp=5.0
Clustering
k-Means 1.0 k=300
nstart=10
Clustering
DensityCut 0.935 alpha=0.13215702947845803
K=3
Clustering
clusterONE 0.0 s=160
d=0.7666666666666667
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=36.781585066443235
maxits=5000
convits=500
Clustering
Markov Clustering 0.0 I=9.483283283283283 Clustering
Transitivity Clustering 1.0 T=36.48703783868393 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering