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=587
samples=20
Clustering
Self Organizing Maps 0.0 x=600
y=600
Clustering
Spectral Clustering 0.0 k=100 Clustering
clusterdp 0.0 k=21
dc=0.9295510122873538
Clustering
HDBSCAN 0.0 minPts=36
k=281
Clustering
AGNES 0.0 method=average
metric=euclidean
k=563
Clustering
c-Means 0.0 k=44
m=2.25
Clustering
k-Medoids (PAM) 0.0 k=449 Clustering
DIANA 0.0 metric=euclidean
k=451
Clustering
DBSCAN 0.0 eps=0.9295510122873538
MinPts=120
Clustering
Hierarchical Clustering 0.0 method=average
k=544
Clustering
fanny 0.0 k=92
membexp=1.1
Clustering
k-Means 0.0 k=525
nstart=10
Clustering
DensityCut 0.0 alpha=0.3059523809523809
K=29
Clustering
clusterONE 1.0 s=580
d=0.2
Clustering
Affinity Propagation 0.0 dampfact=0.9175
preference=13.943265184310308
maxits=2000
convits=200
Clustering
Markov Clustering 1.0 I=9.714914914914916 Clustering
Transitivity Clustering 0.0 T=13.88743629468344 Clustering
MCODE 0.001 v=0.9
cutoff=13.36229580163071
haircut=F
fluff=F
Clustering