Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » veenman_r15 » 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=422
samples=20		 Clustering
Self Organizing Maps 1.0 x=600
y=600		 Clustering
Spectral Clustering 1.0 k=100 Clustering
clusterdp 1.0 k=21
dc=0.9295510122873538		 Clustering
HDBSCAN 1.0 minPts=36
k=241		 Clustering
AGNES 1.0 method=flexible
metric=euclidean
k=461		 Clustering
c-Means 1.0 k=55
m=1.5		 Clustering
k-Medoids (PAM) 1.0 k=596 Clustering
DIANA 1.0 metric=euclidean
k=489		 Clustering
DBSCAN 1.0 eps=0.0
MinPts=1		 Clustering
Hierarchical Clustering 1.0 method=average
k=536		 Clustering
fanny 1.0 k=108
membexp=5.0		 Clustering
k-Means 1.0 k=589
nstart=10		 Clustering
DensityCut 1.0 alpha=0.4255952380952381
K=21		 Clustering
clusterONE 0.0 s=340
d=0.2		 Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=10.457448888232731
maxits=5000
convits=500		 Clustering
Markov Clustering 0.0 I=9.118018018018018 Clustering
Transitivity Clustering 1.0 T=13.873479072276723 Clustering
MCODE 0.999 v=0.8
cutoff=13.36229580163071
haircut=T
fluff=T		 Clustering