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=2
samples=20
Clustering
Self Organizing Maps 0.931 x=2
y=1
Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=25
dc=0.5223964153830202
Clustering
HDBSCAN 1.0 minPts=12
k=1
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=3
Clustering
c-Means 1.0 k=4
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=65
Clustering
DBSCAN 1.0 eps=0.7313549815362281
MinPts=191
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=14
membexp=1.1
Clustering
k-Means 1.0 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.21825396825396826
K=20
Clustering
clusterONE 1.0 s=158
d=0.16666666666666666
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.3917973115372651
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=4.423023023023023 Clustering
Transitivity Clustering 1.0 T=0.947529834508541 Clustering
MCODE 0.852 v=0.9
cutoff=0.5223964153830202
haircut=F
fluff=F
Clustering