Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » synthetic_cuboid » Best Parameters
Navigation:
027Hints:

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=2
samples=20		 Clustering
Self Organizing Maps 0.931 x=2
y=1		 Clustering
Spectral Clustering 1.0 k=14 Clustering
clusterdp 1.0 k=3
dc=0.6791153399979262		 Clustering
HDBSCAN 1.0 minPts=12
k=1		 Clustering
AGNES 1.0 method=average
metric=euclidean
k=3		 Clustering
c-Means 1.0 k=3
m=3.5		 Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1		 Clustering
DBSCAN 1.0 eps=0.9403135476894363
MinPts=250		 Clustering
Hierarchical Clustering 1.0 method=average
k=5		 Clustering
fanny 1.0 k=2
membexp=1.1		 Clustering
k-Means 1.0 k=5
nstart=10		 Clustering
DensityCut 1.0 alpha=0.6666666666666666
K=226		 Clustering
clusterONE 1.0 s=42
d=0.8333333333333334		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.7835946230745302
maxits=2750
convits=500		 Clustering
Markov Clustering 1.0 I=5.447547547547548 Clustering
Transitivity Clustering 1.0 T=0.1364819463613296 Clustering
MCODE 0.852 v=0.9
cutoff=0.5223964153830202
haircut=T
fluff=T		 Clustering