Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » synthetic_cassini » 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 0.0 metric=euclidean
k=104
samples=20		 Clustering
Self Organizing Maps 0.0 x=2
y=50		 Clustering
Spectral Clustering 0.0 k=18 Clustering
clusterdp 0.0 k=11
dc=2.6108040829164603		 Clustering
HDBSCAN 0.0 minPts=72
k=143		 Clustering
AGNES 0.0 method=average
metric=euclidean
k=44		 Clustering
c-Means 0.0 k=234
m=1.5		 Clustering
k-Medoids (PAM) 0.0 k=245 Clustering
DIANA 0.0 metric=euclidean
k=113		 Clustering
DBSCAN 0.0 eps=2.6108040829164603
MinPts=158		 Clustering
Hierarchical Clustering 0.0 method=complete
k=97		 Clustering
fanny 0.0 k=250
membexp=5.0		 Clustering
k-Means 0.0 k=121
nstart=10		 Clustering
DensityCut 0.0 alpha=0.95
K=15		 Clustering
clusterONE 1.0 s=50
d=0.8333333333333334		 Clustering
Affinity Propagation 0.0 dampfact=0.7725
preference=0.9790515310936726
maxits=3500
convits=200		 Clustering
Markov Clustering 1.0 I=1.4296296296296296 Clustering
Transitivity Clustering 0.0 T=3.065538727988997 Clustering
MCODE 0.001 v=0.9
cutoff=3.589855614010133
haircut=T
fluff=T		 Clustering