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=25
dc=0.20895856615320804		 Clustering
HDBSCAN 1.0 minPts=8
k=3		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=8		 Clustering
c-Means 1.0 k=4
m=3.5		 Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1		 Clustering
DBSCAN 1.0 eps=0.15671892461490605
MinPts=125		 Clustering
Hierarchical Clustering 1.0 method=average
k=5		 Clustering
fanny 1.0 k=6
membexp=1.1		 Clustering
k-Means 1.0 k=3
nstart=10		 Clustering
DensityCut 1.0 alpha=0.01984126984126984
K=39		 Clustering
clusterONE 1.0 s=158
d=0.16666666666666666		 Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.3917973115372651
maxits=5000
convits=500		 Clustering
Markov Clustering 1.0 I=4.467567567567568 Clustering
Transitivity Clustering 1.0 T=0.17726965446931314 Clustering
MCODE 0.852 v=0.9
cutoff=0.5223964153830202
haircut=F
fluff=F		 Clustering