Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » synthetic_cuboid » 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=127
samples=20		 Clustering
Self Organizing Maps 1.0 x=35
y=34		 Clustering
Spectral Clustering 0.996 k=25 Clustering
clusterdp 1.0 k=22
dc=0.47015677384471816		 Clustering
HDBSCAN 1.0 minPts=250
k=1		 Clustering
AGNES 1.0 method=complete
metric=euclidean
k=6		 Clustering
c-Means 1.0 k=147
m=1.5		 Clustering
k-Medoids (PAM) 1.0 k=225 Clustering
DIANA 1.0 metric=euclidean
k=126		 Clustering
DBSCAN 1.0 eps=1.5149496046107584
MinPts=183		 Clustering
Hierarchical Clustering 1.0 method=average
k=171		 Clustering
fanny 1.0 k=44
membexp=1.1		 Clustering
k-Means 1.0 k=130
nstart=10		 Clustering
DensityCut 1.0 alpha=0.046665736607142856
K=11		 Clustering
clusterONE 0.0 s=183
d=0.7666666666666667		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=1.5671892461490604
maxits=4250
convits=425		 Clustering
Markov Clustering 0.0 I=3.1757757757757763 Clustering
Transitivity Clustering 1.0 T=1.0087113966705163 Clustering
MCODE 1.0 v=0.2
cutoff=1.1753919346117954
haircut=F
fluff=T		 Clustering