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=23
dc=0.3134378492298121		 Clustering
HDBSCAN 1.0 minPts=10
k=1		 Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=4		 Clustering
c-Means 1.0 k=3
m=2.25		 Clustering
k-Medoids (PAM) 1.0 k=4 Clustering
DIANA 1.0 metric=euclidean
k=1		 Clustering
DBSCAN 1.0 eps=0.8358342646128322
MinPts=75		 Clustering
Hierarchical Clustering 1.0 method=average
k=5		 Clustering
fanny 1.0 k=2
membexp=1.1		 Clustering
k-Means 1.0 k=5
nstart=10		 Clustering
DensityCut 1.0 alpha=0.1388888888888889
K=25		 Clustering
clusterONE 1.0 s=100
d=0.16666666666666666		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.7835946230745302
maxits=2750
convits=500		 Clustering
Markov Clustering 1.0 I=4.904104104104105 Clustering
Transitivity Clustering 1.0 T=0.15530704241116816 Clustering
MCODE 0.852 v=0.4
cutoff=0.5223964153830202
haircut=F
fluff=T		 Clustering