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 0.442 metric=euclidean
k=41
samples=20		 Clustering
Self Organizing Maps 0.862 x=2
y=1		 Clustering
Spectral Clustering 0.816 k=2 Clustering
clusterdp 0.473 k=25
dc=10.77036336360095		 Clustering
HDBSCAN 0.231 minPts=27
k=200		 Clustering
AGNES 0.631 method=flexible
metric=euclidean
k=2		 Clustering
c-Means 0.839 k=2
m=2.25		 Clustering
k-Medoids (PAM) 0.442 k=4 Clustering
DIANA 0.835 metric=euclidean
k=3		 Clustering
DBSCAN 0.234 eps=14.686859132183113
MinPts=7		 Clustering
Hierarchical Clustering 0.524 method=average
k=12		 Clustering
fanny 0.838 k=2
membexp=1.99		 Clustering
k-Means 0.838 k=3
nstart=10		 Clustering
DensityCut 0.318 alpha=0.0
K=2		 Clustering
clusterONE 0.684 s=18
d=0.16666666666666666		 Clustering
Markov Clustering 0.0 I=1.2336336336336338 Clustering
Transitivity Clustering 0.459 T=4.572185375484433 Clustering
MCODE 0.242 v=0.5
cutoff=4.895619710727704
haircut=T
fluff=F		 Clustering