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=14
samples=20		 Clustering
Self Organizing Maps 1.0 x=76
y=183		 Clustering
Spectral Clustering 1.0 k=18 Clustering
clusterdp 1.0 k=21
dc=1.8275628580415222		 Clustering
HDBSCAN 1.0 minPts=36
k=250		 Clustering
AGNES 1.0 method=complete
metric=euclidean
k=27		 Clustering
c-Means 1.0 k=74
m=1.5		 Clustering
k-Medoids (PAM) 1.0 k=175 Clustering
DIANA 1.0 metric=euclidean
k=51		 Clustering
DBSCAN 1.0 eps=2.741344287062283
MinPts=191		 Clustering
Hierarchical Clustering 1.0 method=complete
k=244		 Clustering
fanny 1.0 k=31
membexp=5.0		 Clustering
k-Means 1.0 k=160
nstart=10		 Clustering
DensityCut 1.0 alpha=1.0
K=12		 Clustering
clusterONE 0.0 s=67
d=0.03333333333333333		 Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=3.9162061243746904
maxits=4250
convits=500		 Clustering
Markov Clustering 0.0 I=4.316116116116116 Clustering
Transitivity Clustering 1.0 T=3.6653180443346702 Clustering
MCODE 0.999 v=0.6
cutoff=3.589855614010133
haircut=T
fluff=T		 Clustering