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=72
samples=20
Clustering
Self Organizing Maps 1.0 x=260
y=260
Clustering
Spectral Clustering 1.0 k=30 Clustering
clusterdp 1.0 k=15
dc=17.174394377159917
Clustering
HDBSCAN 1.0 minPts=4
k=78
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=258
Clustering
c-Means 1.0 k=211
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=206 Clustering
DIANA 1.0 metric=euclidean
k=166
Clustering
DBSCAN 1.0 eps=0.0
MinPts=11
Clustering
Hierarchical Clustering 1.0 method=complete
k=304
Clustering
fanny 1.0 k=31
membexp=2.0
Clustering
k-Means 1.0 k=143
nstart=10
Clustering
DensityCut 1.0 alpha=0.01984126984126984
K=7
Clustering
clusterONE 0.0 s=115
d=0.43333333333333335
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=30.307754783223388
maxits=2750
convits=200
Clustering
Markov Clustering 0.0 I=3.9241241241241243 Clustering
Transitivity Clustering 1.0 T=28.42679302490522 Clustering
MCODE 1.0 v=0.2
cutoff=26.519285435320466
haircut=F
fluff=T
Clustering