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=60
samples=20		 Clustering
Self Organizing Maps 1.0 x=2
y=9		 Clustering
Spectral Clustering 1.0 k=18 Clustering
clusterdp 1.0 k=22
dc=1.6970226538956992		 Clustering
HDBSCAN 1.0 minPts=36
k=238		 Clustering
AGNES 1.0 method=average
metric=euclidean
k=46		 Clustering
c-Means 1.0 k=51
m=1.01		 Clustering
k-Medoids (PAM) 1.0 k=243 Clustering
DIANA 1.0 metric=euclidean
k=82		 Clustering
DBSCAN 1.0 eps=2.741344287062283
MinPts=191		 Clustering
Hierarchical Clustering 1.0 method=average
k=172		 Clustering
fanny 1.0 k=117
membexp=5.0		 Clustering
k-Means 1.0 k=218
nstart=10		 Clustering
DensityCut 1.0 alpha=0.98125
K=6		 Clustering
clusterONE 0.0 s=233
d=0.8333333333333334		 Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=1.9581030621873452
maxits=5000
convits=350		 Clustering
Markov Clustering 0.0 I=4.262662662662662 Clustering
Transitivity Clustering 1.0 T=2.751928627938972 Clustering
MCODE 0.999 v=0.9
cutoff=3.589855614010133
haircut=F
fluff=F		 Clustering