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=14
samples=20		 Clustering
Self Organizing Maps 1.0 x=18
y=84		 Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=16
dc=1.2015117553809462		 Clustering
HDBSCAN 1.0 minPts=35
k=54		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=27		 Clustering
c-Means 1.0 k=4
m=1.01		 Clustering
k-Medoids (PAM) 1.0 k=181 Clustering
DIANA 1.0 metric=euclidean
k=97		 Clustering
DBSCAN 1.0 eps=0.05223964153830201
MinPts=84		 Clustering
Hierarchical Clustering 1.0 method=average
k=42		 Clustering
fanny 1.0 k=77
membexp=5.0		 Clustering
k-Means 1.0 k=112
nstart=10		 Clustering
DensityCut 1.0 alpha=0.025829081632653062
K=19		 Clustering
clusterONE 0.0 s=183
d=0.26666666666666666		 Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=0.7835946230745302
maxits=3500
convits=500		 Clustering
Markov Clustering 0.0 I=2.2225225225225227 Clustering
Transitivity Clustering 1.0 T=1.5593454561282944 Clustering
MCODE 1.0 v=0.4
cutoff=1.3059910384575504
haircut=F
fluff=F		 Clustering