Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » twonorm_50d » 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=187
samples=20		 Clustering
Self Organizing Maps 0.0 x=173
y=34		 Clustering
Spectral Clustering 0.004 k=59 Clustering
clusterdp 0.022 k=24
dc=4.406057739654934		 Clustering
HDBSCAN 0.0 minPts=166
k=200		 Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=197		 Clustering
c-Means 0.0 k=37
m=3.5		 Clustering
k-Medoids (PAM) 0.0 k=190 Clustering
DIANA 0.0 metric=euclidean
k=186		 Clustering
DBSCAN 0.0 eps=4.406057739654934
MinPts=47		 Clustering
Hierarchical Clustering 0.0 method=average
k=188		 Clustering
fanny 0.0 k=54
membexp=2.2866666666666666		 Clustering
k-Means 0.0 k=197
nstart=10		 Clustering
DensityCut 0.083 alpha=1.52587890625E-5
K=2		 Clustering
clusterONE 0.006 s=3
d=0.7		 Clustering
Markov Clustering 1.0 I=1.6790790790790793 Clustering
Transitivity Clustering 0.0 T=13.23140462358839 Clustering
MCODE 0.052 v=0.1
cutoff=4.895619710727704
haircut=T
fluff=F		 Clustering