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=101
samples=20		 Clustering
Self Organizing Maps 0.0 x=241
y=125		 Clustering
Spectral Clustering 0.0 k=17 Clustering
clusterdp 0.0 k=22
dc=1.4359422456040531		 Clustering
HDBSCAN 0.0 minPts=10
k=155		 Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=67		 Clustering
c-Means 0.0 k=202
m=3.5		 Clustering
k-Medoids (PAM) 0.0 k=93 Clustering
DIANA 0.0 metric=euclidean
k=229		 Clustering
DBSCAN 0.0 eps=1.3054020414582301
MinPts=200		 Clustering
Hierarchical Clustering 0.0 method=complete
k=106		 Clustering
fanny 0.0 k=51
membexp=1.1		 Clustering
k-Means 0.0 k=124
nstart=10		 Clustering
DensityCut 0.0 alpha=0.9374999701976776
K=5		 Clustering
clusterONE 0.643 s=208
d=0.8		 Clustering
Affinity Propagation 0.0 dampfact=0.99
preference=2.9371545932810177
maxits=5000
convits=275		 Clustering
Markov Clustering 0.643 I=6.89079079079079 Clustering
Transitivity Clustering 0.0 T=3.884845114369688 Clustering
MCODE 0.007 v=0
cutoff=1.3054020414582301
haircut=F
fluff=F		 Clustering