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=1
samples=20
Clustering
Self Organizing Maps 0.979 x=2
y=27
Clustering
Spectral Clustering 1.0 k=4 Clustering
clusterdp 1.0 k=5
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=184
k=1
Clustering
AGNES 1.0 method=single
metric=euclidean
k=6
Clustering
c-Means 0.993 k=3
m=1.01
Clustering
k-Medoids (PAM) 0.995 k=2 Clustering
DIANA 1.0 metric=euclidean
k=2
Clustering
DBSCAN 1.0 eps=3.881546083714581
MinPts=709
Clustering
Hierarchical Clustering 1.0 method=average
k=9
Clustering
fanny 1.0 k=6
membexp=2.0
Clustering
k-Means 0.993 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.23333333333333334
K=78
Clustering
clusterONE 1.0 s=446
d=0.7333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering 1.0 I=4.52992992992993 Clustering
Transitivity Clustering 1.0 T=18.68892558825539 Clustering
MCODE 0.818 v=0.0
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering