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 0.734 metric=euclidean
k=2
samples=20
Clustering
Self Organizing Maps 0.732 x=2
y=1
Clustering
Spectral Clustering 0.816 k=4 Clustering
clusterdp 1.0 k=24
dc=1.4758472143145445
Clustering
HDBSCAN 0.956 minPts=2
k=12
Clustering
AGNES 0.956 method=weighted
metric=euclidean
k=10
Clustering
c-Means 0.759 k=3
m=1.5
Clustering
k-Medoids (PAM) 0.745 k=3 Clustering
DIANA 0.75 metric=euclidean
k=2
Clustering
DBSCAN 0.978 eps=12.790675857392719
MinPts=184
Clustering
Hierarchical Clustering 0.956 method=single
k=12
Clustering
fanny 0.773 k=4
membexp=5.0
Clustering
k-Means 0.727 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.6666666666666666
K=12
Clustering
clusterONE 0.536 s=176
d=0.0
Clustering
Affinity Propagation 0.553 dampfact=0.99
preference=0.0
maxits=2000
convits=350
Clustering
Markov Clustering 0.536 I=5.59009009009009 Clustering
Transitivity Clustering 0.769 T=8.228697681413424 Clustering
MCODE 0.492 v=0.2
cutoff=14.143535803847717
haircut=T
fluff=T
Clustering