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=320
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=37
k=121
Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=229
Clustering
c-Means 1.0 k=202
m=1.01
Clustering
k-Medoids (PAM) 1.0 k=154 Clustering
DIANA 1.0 metric=euclidean
k=230
Clustering
DBSCAN 1.0 eps=2.4521056710962155
MinPts=372
Clustering
Hierarchical Clustering 1.0 method=average
k=287
Clustering
fanny 1.0 k=204
membexp=2.0
Clustering
k-Means 1.0 k=268
nstart=10
Clustering
DensityCut 0.935 alpha=0.12152777777777776
K=3
Clustering
clusterONE 0.0 s=345
d=0.0
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=36.781585066443235
maxits=2750
convits=350
Clustering
Markov Clustering 0.0 I=6.1513513513513525 Clustering
Transitivity Clustering 1.0 T=36.48703783868393 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=F
fluff=F
Clustering