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=737
samples=20
Clustering
Self Organizing Maps 0.0 x=788
y=735
Clustering
Spectral Clustering 0.006 k=62 Clustering
clusterdp 0.0 k=13
dc=2.587697389143054
Clustering
HDBSCAN 0.0 minPts=132
k=368
Clustering
AGNES 0.0 method=average
metric=euclidean
k=221
Clustering
c-Means 0.0 k=668
m=3.5
Clustering
k-Medoids (PAM) 0.0 k=716 Clustering
DIANA 0.0 metric=euclidean
k=513
Clustering
DBSCAN 0.0 eps=3.881546083714581
MinPts=630
Clustering
Hierarchical Clustering 0.0 method=average
k=456
Clustering
fanny 0.0 k=262
membexp=2.0
Clustering
k-Means 0.0 k=528
nstart=10
Clustering
DensityCut 0.0 alpha=3.255208333333333E-4
K=6
Clustering
clusterONE 0.783 s=630
d=0.0
Clustering
Affinity Propagation 0.002 dampfact=0.7
preference=9.703865209286453
maxits=2750
convits=200
Clustering
Markov Clustering 0.783 I=2.293793793793794 Clustering
Transitivity Clustering 0.0 T=38.58233494623203 Clustering
MCODE 0.001 v=0.8
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering