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=24
samples=20		 Clustering
Self Organizing Maps 0.0 x=126
y=167		 Clustering
Spectral Clustering 0.046 k=25 Clustering
clusterdp 0.0 k=24
dc=1.5671892461490604		 Clustering
HDBSCAN 0.0 minPts=4
k=6		 Clustering
AGNES 0.0 method=single
metric=euclidean
k=38		 Clustering
c-Means 0.0 k=63
m=2.25		 Clustering
k-Medoids (PAM) 0.0 k=113 Clustering
DIANA 0.0 metric=euclidean
k=124		 Clustering
DBSCAN 0.0 eps=1.3582306799958523
MinPts=200		 Clustering
Hierarchical Clustering 0.0 method=complete
k=235		 Clustering
fanny 0.0 k=117
membexp=1.1		 Clustering
k-Means 0.0 k=104
nstart=10		 Clustering
DensityCut 0.0 alpha=0.0732142857142857
K=9		 Clustering
clusterONE 0.739 s=108
d=0.6333333333333333		 Clustering
Affinity Propagation 0.0 dampfact=0.845
preference=0.3917973115372651
maxits=2750
convits=200		 Clustering
Markov Clustering 0.739 I=8.6992992992993 Clustering
Transitivity Clustering 0.0 T=1.2644189513474902 Clustering
MCODE 0.0 v=0.1
cutoff=1.1753919346117954
haircut=T
fluff=F		 Clustering