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=134
samples=20
Clustering
Self Organizing Maps 1.0 x=399
y=359
Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=3
dc=4.904211342192431
Clustering
HDBSCAN 1.0 minPts=380
k=361
Clustering
AGNES 1.0 method=average
metric=euclidean
k=88
Clustering
c-Means 1.0 k=230
m=3.5
Clustering
k-Medoids (PAM) 1.0 k=151 Clustering
DIANA 1.0 metric=euclidean
k=251
Clustering
DBSCAN 1.0 eps=12.260528355481078
MinPts=306
Clustering
Hierarchical Clustering 1.0 method=single
k=174
Clustering
fanny 1.0 k=78
membexp=5.0
Clustering
k-Means 1.0 k=129
nstart=10
Clustering
DensityCut 0.935 alpha=0.19593253968253968
K=3
Clustering
clusterONE 0.0 s=27
d=0.8666666666666667
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=36.781585066443235
maxits=2000
convits=500
Clustering
Markov Clustering 0.0 I=3.1757757757757763 Clustering
Transitivity Clustering 1.0 T=36.04521699704497 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=F
Clustering