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=13
samples=20		 Clustering
Self Organizing Maps 0.0 x=250
y=233		 Clustering
Spectral Clustering 0.004 k=25 Clustering
clusterdp 0.0 k=25
dc=0.05223964153830201		 Clustering
HDBSCAN 0.0 minPts=36
k=119		 Clustering
AGNES 0.0 method=average
metric=euclidean
k=161		 Clustering
c-Means 0.0 k=60
m=2.25		 Clustering
k-Medoids (PAM) 0.0 k=6 Clustering
DIANA 0.0 metric=euclidean
k=53		 Clustering
DBSCAN 0.0 eps=0.5746360569213221
MinPts=216		 Clustering
Hierarchical Clustering 0.0 method=average
k=57		 Clustering
fanny 0.0 k=39
membexp=5.0		 Clustering
k-Means 0.0 k=222
nstart=10		 Clustering
DensityCut 0.0 alpha=0.025829081632653062
K=6		 Clustering
clusterONE 1.0 s=191
d=0.3		 Clustering
Affinity Propagation 0.0 dampfact=0.99
preference=0.0
maxits=5000
convits=500		 Clustering
Markov Clustering 1.0 I=4.6902902902902905 Clustering
Transitivity Clustering 0.0 T=1.549932908103375 Clustering
MCODE 0.0 v=0.2
cutoff=1.3059910384575504
haircut=T
fluff=F		 Clustering