Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » chang_pathbased » 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=106
samples=20		 Clustering
Self Organizing Maps 0.0 x=270
y=200		 Clustering
Spectral Clustering 0.0 k=8 Clustering
clusterdp 0.0 k=23
dc=0.9765301383526837		 Clustering
HDBSCAN 0.0 minPts=10
k=290		 Clustering
AGNES 0.0 method=average
metric=euclidean
k=114		 Clustering
c-Means 0.0 k=29
m=1.01		 Clustering
k-Medoids (PAM) 0.0 k=102 Clustering
DIANA 0.0 metric=euclidean
k=73		 Clustering
DBSCAN 0.0 eps=9.765301383526836
MinPts=240		 Clustering
Hierarchical Clustering 0.0 method=single
k=161		 Clustering
fanny 0.0 k=136
membexp=1.1		 Clustering
k-Means 0.0 k=213
nstart=10		 Clustering
DensityCut 0.0 alpha=0.1040107709750567
K=2		 Clustering
clusterONE 1.0 s=240
d=0.5		 Clustering
Affinity Propagation 0.0 dampfact=0.7725
preference=29.29590415058051
maxits=3500
convits=275		 Clustering
Markov Clustering 0.352 I=9.857457457457457 Clustering
Transitivity Clustering 0.0 T=28.034919287242207 Clustering