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=216
samples=20		 Clustering
Self Organizing Maps 0.0 x=150
y=142		 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=flexible
metric=euclidean
k=210		 Clustering
c-Means 0.0 k=124
m=2.25		 Clustering
k-Medoids (PAM) 0.0 k=49 Clustering
DIANA 0.0 metric=euclidean
k=39		 Clustering
DBSCAN 0.0 eps=3.0024246953539295
MinPts=208		 Clustering
Hierarchical Clustering 0.0 method=complete
k=161		 Clustering
fanny 0.0 k=84
membexp=5.0		 Clustering
k-Means 0.0 k=240
nstart=10		 Clustering
DensityCut 0.0 alpha=0.9352678571428571
K=6		 Clustering
clusterONE 0.643 s=200
d=0.5		 Clustering
Affinity Propagation 0.0 dampfact=0.99
preference=3.9162061243746904
maxits=2750
convits=200		 Clustering
Markov Clustering 0.643 I=5.251551551551553 Clustering
Transitivity Clustering 0.0 T=3.582995393071539 Clustering
MCODE 0.007 v=0
cutoff=1.3054020414582301
haircut=F
fluff=F		 Clustering