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=116
samples=20		 Clustering
Self Organizing Maps 1.0 x=250
y=250		 Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=25
dc=1.5671892461490604		 Clustering
HDBSCAN 1.0 minPts=178
k=250		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=70		 Clustering
c-Means 1.0 k=37
m=1.01		 Clustering
k-Medoids (PAM) 1.0 k=77 Clustering
DIANA 1.0 metric=euclidean
k=243		 Clustering
DBSCAN 1.0 eps=0.6791153399979262
MinPts=191		 Clustering
Hierarchical Clustering 1.0 method=single
k=76		 Clustering
fanny 1.0 k=73
membexp=2.0		 Clustering
k-Means 1.0 k=84
nstart=10		 Clustering
DensityCut 1.0 alpha=0.060693027210884355
K=8		 Clustering
clusterONE 0.0 s=225
d=0.7		 Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=1.5671892461490604
maxits=5000
convits=425		 Clustering
Markov Clustering 0.0 I=1.58998998998999 Clustering
Transitivity Clustering 1.0 T=1.1561746490609186 Clustering
MCODE 1.0 v=0.7
cutoff=1.2406914865346728
haircut=T
fluff=T		 Clustering