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=250
y=241
Clustering
Spectral Clustering 1.0 k=13 Clustering
clusterdp 1.0 k=15
dc=1.9872000000000003
Clustering
HDBSCAN 1.0 minPts=8
k=7
Clustering
AGNES 1.0 method=single
metric=euclidean
k=113
Clustering
c-Means 1.0 k=37
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=74 Clustering
DIANA 1.0 metric=euclidean
k=94
Clustering
DBSCAN 1.0 eps=2.6496
MinPts=158
Clustering
Hierarchical Clustering 1.0 method=single
k=43
Clustering
fanny 1.0 k=121
membexp=5.0
Clustering
k-Means 1.0 k=85
nstart=10
Clustering
DensityCut 1.0 alpha=0.06904761904761904
K=5
Clustering
clusterONE 0.0 s=167
d=0.4666666666666667
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=3.3120000000000003
maxits=4250
convits=350
Clustering
Markov Clustering 0.5 I=9.35855855855856 Clustering
Transitivity Clustering 1.0 T=3.278846846846847 Clustering
MCODE 0.999 v=0.7
cutoff=3.036
haircut=F
fluff=F
Clustering