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=12
samples=20		 Clustering
Self Organizing Maps 1.0 x=233
y=233		 Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=7
dc=1.5671892461490604		 Clustering
HDBSCAN 1.0 minPts=25
k=74		 Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=64		 Clustering
c-Means 1.0 k=4
m=5.0		 Clustering
k-Medoids (PAM) 1.0 k=130 Clustering
DIANA 1.0 metric=euclidean
k=78		 Clustering
DBSCAN 1.0 eps=0.0
MinPts=108		 Clustering
Hierarchical Clustering 1.0 method=average
k=137		 Clustering
fanny 1.0 k=89
membexp=1.1		 Clustering
k-Means 1.0 k=159
nstart=10		 Clustering
DensityCut 1.0 alpha=0.06101190476190475
K=24		 Clustering
clusterONE 0.0 s=175
d=0.9333333333333333		 Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=1.5671892461490604
maxits=2000
convits=275		 Clustering
Markov Clustering 0.0 I=1.8127127127127127 Clustering
Transitivity Clustering 1.0 T=0.991455058624831 Clustering
MCODE 1.0 v=0.4
cutoff=1.3059910384575504
haircut=F
fluff=F		 Clustering