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=166
samples=20
Clustering
Self Organizing Maps 1.0 x=57
y=144
Clustering
Spectral Clustering 0.998 k=34 Clustering
clusterdp 1.0 k=7
dc=2.459745357190907
Clustering
HDBSCAN 1.0 minPts=2
k=38
Clustering
AGNES 1.0 method=average
metric=euclidean
k=210
Clustering
c-Means 1.0 k=90
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=237 Clustering
DIANA 1.0 metric=euclidean
k=240
Clustering
DBSCAN 1.0 eps=0.49194907143818145
MinPts=16
Clustering
Hierarchical Clustering 1.0 method=complete
k=177
Clustering
fanny 1.0 k=95
membexp=1.1
Clustering
k-Means 1.0 k=104
nstart=10
Clustering
DensityCut 1.0 alpha=0.5214285714285714
K=9
Clustering
clusterONE 0.0 s=80
d=0.8333333333333334
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=14.758472143145443
maxits=2750
convits=200
Clustering
Markov Clustering 0.0 I=2.0265265265265264 Clustering
Transitivity Clustering 1.0 T=14.758472143145443 Clustering
MCODE 0.991 v=0.3
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering