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 1.0 metric=euclidean
k=135
samples=20		 Clustering
Self Organizing Maps 1.0 x=26
y=150		 Clustering
Spectral Clustering 1.0 k=17 Clustering
clusterdp 1.0 k=10
dc=0.522160816583292		 Clustering
HDBSCAN 1.0 minPts=5
k=113		 Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=28		 Clustering
c-Means 1.0 k=92
m=5.0		 Clustering
k-Medoids (PAM) 1.0 k=229 Clustering
DIANA 1.0 metric=euclidean
k=155		 Clustering
DBSCAN 1.0 eps=1.5664824497498762
MinPts=225		 Clustering
Hierarchical Clustering 1.0 method=single
k=239		 Clustering
fanny 1.0 k=95
membexp=5.0		 Clustering
k-Means 1.0 k=53
nstart=10		 Clustering
DensityCut 1.0 alpha=0.9375
K=12		 Clustering
clusterONE 0.0 s=50
d=0.7		 Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=0.0
maxits=5000
convits=425		 Clustering
Markov Clustering 0.0 I=3.567767767767768 Clustering
Transitivity Clustering 1.0 T=2.7048871129314676 Clustering
MCODE 0.999 v=0.9
cutoff=3.589855614010133
haircut=F
fluff=F		 Clustering