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=55
samples=20
Clustering
Self Organizing Maps 0.931 x=2
y=1
Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=4
dc=1.5671892461490604
Clustering
HDBSCAN 1.0 minPts=7
k=1
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=4
Clustering
c-Means 1.0 k=4
m=2.25
Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=0.8358342646128322
MinPts=167
Clustering
Hierarchical Clustering 1.0 method=average
k=5
Clustering
fanny 1.0 k=2
membexp=1.1
Clustering
k-Means 1.0 k=5
nstart=10
Clustering
DensityCut 1.0 alpha=0.25793650793650796
K=39
Clustering
clusterONE 1.0 s=158
d=0.9
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.3917973115372651
maxits=5000
convits=500
Clustering
Markov Clustering 1.0 I=5.385185185185186 Clustering
Transitivity Clustering 1.0 T=0.8879170303507189 Clustering
MCODE 0.852 v=0.3
cutoff=0.5223964153830202
haircut=T
fluff=T
Clustering