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=188
samples=20
Clustering
Self Organizing Maps 0.0 x=385
y=239
Clustering
Spectral Clustering 0.001 k=24 Clustering
clusterdp 0.009 k=3
dc=4.904211342192431
Clustering
HDBSCAN 0.0 minPts=19
k=361
Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=253
Clustering
c-Means 0.0 k=386
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=298 Clustering
DIANA 0.0 metric=euclidean
k=198
Clustering
DBSCAN 0.0 eps=7.356317013288647
MinPts=332
Clustering
Hierarchical Clustering 0.0 method=average
k=119
Clustering
fanny 0.0 k=128
membexp=1.1
Clustering
k-Means 0.0 k=228
nstart=10
Clustering
DensityCut 0.065 alpha=0.15341553287981857
K=3
Clustering
clusterONE 1.0 s=345
d=0.8
Clustering
Affinity Propagation 0.0 dampfact=0.7725
preference=36.781585066443235
maxits=4250
convits=500
Clustering
Markov Clustering 1.0 I=1.1890890890890893 Clustering
Transitivity Clustering 0.0 T=35.60339615540602 Clustering
MCODE 0.004 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=F
Clustering