Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » zahn_compound » 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=311
samples=20		 Clustering
Self Organizing Maps 1.0 x=385
y=239		 Clustering
Spectral Clustering 0.999 k=24 Clustering
clusterdp 0.991 k=3
dc=4.904211342192431		 Clustering
HDBSCAN 1.0 minPts=304
k=399		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=394		 Clustering
c-Means 1.0 k=29
m=5.0		 Clustering
k-Medoids (PAM) 1.0 k=212 Clustering
DIANA 1.0 metric=euclidean
k=269		 Clustering
DBSCAN 1.0 eps=7.356317013288647
MinPts=332		 Clustering
Hierarchical Clustering 1.0 method=complete
k=378		 Clustering
fanny 1.0 k=172
membexp=1.1		 Clustering
k-Means 1.0 k=339
nstart=10		 Clustering
DensityCut 0.935 alpha=0.12152777777777776
K=3		 Clustering
clusterONE 0.0 s=14
d=0.06666666666666667		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=36.781585066443235
maxits=2750
convits=425		 Clustering
Markov Clustering 0.0 I=9.207107107107106 Clustering
Transitivity Clustering 1.0 T=35.97158019010514 Clustering
MCODE 0.996 v=0.1
cutoff=32.183886933137835
haircut=T
fluff=T		 Clustering