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 0.0 metric=euclidean
k=23
samples=20		 Clustering
Self Organizing Maps 0.0 x=10
y=133		 Clustering
Spectral Clustering 0.004 k=25 Clustering
clusterdp 0.0 k=20
dc=0.05223964153830201		 Clustering
HDBSCAN 0.0 minPts=226
k=226		 Clustering
AGNES 0.0 method=average
metric=euclidean
k=250		 Clustering
c-Means 0.0 k=24
m=1.01		 Clustering
k-Medoids (PAM) 0.0 k=6 Clustering
DIANA 0.0 metric=euclidean
k=117		 Clustering
DBSCAN 0.0 eps=1.3582306799958523
MinPts=200		 Clustering
Hierarchical Clustering 0.0 method=average
k=189		 Clustering
fanny 0.0 k=5
membexp=1.1		 Clustering
k-Means 0.0 k=19
nstart=10		 Clustering
DensityCut 0.0 alpha=0.15873015873015872
K=35		 Clustering
clusterONE 1.0 s=34
d=0.6666666666666666		 Clustering
Affinity Propagation 0.0 dampfact=0.7
preference=0.7835946230745302
maxits=4250
convits=200		 Clustering
Markov Clustering 1.0 I=8.102402402402403 Clustering
Transitivity Clustering 0.0 T=0.9977300906414439 Clustering
MCODE 0.0 v=0.1
cutoff=1.1753919346117954
haircut=F
fluff=F		 Clustering