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 0.841 metric=euclidean
k=6
samples=20
Clustering
Self Organizing Maps 0.845 x=2
y=1
Clustering
Spectral Clustering 0.889 k=4 Clustering
clusterdp 1.0 k=24
dc=1.4758472143145445
Clustering
HDBSCAN 0.972 minPts=2
k=12
Clustering
AGNES 0.972 method=weighted
metric=euclidean
k=10
Clustering
c-Means 0.857 k=2
m=1.01
Clustering
k-Medoids (PAM) 0.853 k=2 Clustering
DIANA 0.857 metric=euclidean
k=2
Clustering
DBSCAN 0.987 eps=12.790675857392719
MinPts=184
Clustering
Hierarchical Clustering 0.972 method=single
k=12
Clustering
fanny 0.869 k=3
membexp=2.0
Clustering
k-Means 0.841 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.6464285714285714
K=12
Clustering
clusterONE 0.689 s=232
d=0.8
Clustering
Affinity Propagation 0.689 dampfact=0.7725
preference=0.0
maxits=2750
convits=275
Clustering
Markov Clustering 0.689 I=3.8350350350350353 Clustering
Transitivity Clustering 0.866 T=8.228697681413424 Clustering
MCODE 0.598 v=0.0
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering