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=526
samples=20
Clustering
Self Organizing Maps 0.0 x=600
y=60
Clustering
Spectral Clustering 0.013 k=39 Clustering
clusterdp 0.003 k=17
dc=0.9295510122873538
Clustering
HDBSCAN 0.0 minPts=29
k=514
Clustering
AGNES 0.0 method=average
metric=euclidean
k=563
Clustering
c-Means 0.0 k=467
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=598 Clustering
DIANA 0.0 metric=euclidean
k=473
Clustering
DBSCAN 0.0 eps=0.9295510122873538
MinPts=500
Clustering
Hierarchical Clustering 0.0 method=complete
k=464
Clustering
fanny 0.0 k=268
membexp=2.0
Clustering
k-Means 0.0 k=384
nstart=10
Clustering
DensityCut 0.007 alpha=0.40595238095238095
K=15
Clustering
clusterONE 0.935 s=440
d=0.06666666666666667
Clustering
Affinity Propagation 0.004 dampfact=0.99
preference=10.457448888232731
maxits=2750
convits=200
Clustering
Markov Clustering 0.935 I=9.242742742742744 Clustering
Transitivity Clustering 0.0 T=13.88743629468344 Clustering
MCODE 0.017 v=0.4
cutoff=12.781326418951116
haircut=T
fluff=F
Clustering