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 0.5 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.408 x=2
y=1
Clustering
Spectral Clustering 0.93 k=3 Clustering
clusterdp 1.0 k=3
dc=4.041033971096452
Clustering
HDBSCAN 0.947 minPts=5
k=34
Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=3
Clustering
c-Means 0.441 k=11
m=1.01
Clustering
k-Medoids (PAM) 0.418 k=9 Clustering
DIANA 0.5 metric=euclidean
k=4
Clustering
DBSCAN 1.0 eps=14.14361889883758
MinPts=280
Clustering
Hierarchical Clustering 1.0 method=complete
k=3
Clustering
fanny 0.5 k=8
membexp=2.0
Clustering
k-Means 0.463 k=7
nstart=10
Clustering
DensityCut 1.0 alpha=0.09375
K=6
Clustering
clusterONE 0.5 s=1
d=0.0
Clustering
Affinity Propagation 0.5 dampfact=0.7725
preference=7.576938695805847
maxits=4250
convits=200
Clustering
Markov Clustering 0.5 I=3.1757757757757763 Clustering
Transitivity Clustering 0.5 T=0.06067618575219898 Clustering
MCODE 0.484 v=0.3
cutoff=17.679523623546974
haircut=T
fluff=F
Clustering