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=535
samples=20
Clustering
Self Organizing Maps 1.0 x=600
y=560
Clustering
Spectral Clustering 1.0 k=100 Clustering
clusterdp 1.0 k=25
dc=0.9295510122873538
Clustering
HDBSCAN 1.0 minPts=86
k=200
Clustering
AGNES 1.0 method=average
metric=euclidean
k=417
Clustering
c-Means 1.0 k=104
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=514 Clustering
DIANA 1.0 metric=euclidean
k=562
Clustering
DBSCAN 1.0 eps=0.9295510122873538
MinPts=120
Clustering
Hierarchical Clustering 1.0 method=single
k=540
Clustering
fanny 1.0 k=180
membexp=1.1
Clustering
k-Means 1.0 k=588
nstart=10
Clustering
DensityCut 1.0 alpha=0.3809514272780645
K=28
Clustering
clusterONE 0.0 s=300
d=0.7333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.9175
preference=13.943265184310308
maxits=5000
convits=425
Clustering
Markov Clustering 0.0 I=2.4986986986986985 Clustering
Transitivity Clustering 1.0 T=13.859521849870005 Clustering
MCODE 0.999 v=0.8
cutoff=13.36229580163071
haircut=T
fluff=T
Clustering