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=198
samples=20
Clustering
Self Organizing Maps 1.0 x=630
y=263
Clustering
Spectral Clustering 1.0 k=118 Clustering
clusterdp 1.0 k=21
dc=2.587697389143054
Clustering
HDBSCAN 1.0 minPts=237
k=761
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=22
Clustering
c-Means 1.0 k=105
m=5.0
Clustering
k-Medoids (PAM) 1.0 k=520 Clustering
DIANA 1.0 metric=euclidean
k=614
Clustering
DBSCAN 1.0 eps=3.881546083714581
MinPts=630
Clustering
Hierarchical Clustering 1.0 method=complete
k=470
Clustering
fanny 1.0 k=271
membexp=5.0
Clustering
k-Means 1.0 k=358
nstart=10
Clustering
DensityCut 1.0 alpha=0.0
K=9
Clustering
clusterONE 0.0 s=27
d=0.23333333333333334
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=38.815460837145814
maxits=5000
convits=500
Clustering
Markov Clustering 0.0 I=7.015515515515515 Clustering
Transitivity Clustering 1.0 T=37.64983138257687 Clustering
MCODE 1.0 v=0.9
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering