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 0.39 metric=euclidean
k=67
samples=20
Clustering
Self Organizing Maps 0.372 x=134
y=100
Clustering
Spectral Clustering 0.325 k=5 Clustering
clusterdp 0.136 k=12
dc=0.0
Clustering
HDBSCAN 0.184 minPts=2
k=4
Clustering
AGNES 0.395 method=ward
metric=euclidean
k=57
Clustering
c-Means 0.397 k=52
m=1.5
Clustering
k-Medoids (PAM) 0.408 k=60 Clustering
DIANA 0.354 metric=euclidean
k=86
Clustering
DBSCAN 0.016 eps=0.552
MinPts=17
Clustering
Hierarchical Clustering 0.394 method=average
k=55
Clustering
fanny NaN k=75
membexp=1.1
Clustering
k-Means 0.407 k=66
nstart=10
Clustering
DensityCut 0.277 alpha=0.38095238095238093
K=24
Clustering
clusterONE NaN s=208
d=0.6
Clustering
Affinity Propagation 0.366 dampfact=0.99
preference=2.484
maxits=4250
convits=350
Clustering
Markov Clustering 0.329 I=8.601301301301302 Clustering
Transitivity Clustering 0.403 T=3.0567207207207208 Clustering
MCODE 0.288 v=0.1
cutoff=3.036
haircut=F
fluff=T
Clustering