Clust
Eval
clustering evaluation framework
Welcome
Overview
Clustering Methods
Data Sets
Measures
Submit
Advanced
Help
About us
Location:
Data Sets
»
astral1_161
»
Best Parameters
Navigation:
General
Statistics
Best Qualities
Best Parameters
All Clusterings
Hints:
Which parameter sets lead to the optimal clustering quality?
Please choose a clustering quality measure:
Davies Bouldin Index (R)
Dunn Index (R)
F1-Score
F2-Score
False Discovery Rate
False Positive Rate
Fowlkes Mallows Index (R)
Jaccard Index (R)
Rand Index
Rand Index (R)
Sensitivity
Silhouette Value (R)
Specificity
V-Measure
Program
Best quality
Parameter set
Clustering
Spectral Clustering
0.78
k=3
Clustering
clusterdp
0.726
k=11
dc=278.6055634074976
Clustering
AGNES
0.764
method=average
metric=euclidean
k=50
Clustering
c-Means
0.663
k=9
m=5.0
Clustering
k-Medoids (PAM)
0.657
k=11
Clustering
DIANA
0.481
metric=euclidean
k=92
Clustering
DBSCAN
0.287
eps=0.0
MinPts=254
Clustering
Hierarchical Clustering
0.747
method=complete
k=52
Clustering
fanny
0.494
k=20
membexp=1.1
Clustering
clusterONE
0.317
s=1
d=0.06666666666666667
Clustering
Affinity Propagation
0.267
dampfact=0.7725
preference=0.0
maxits=2000
convits=275
Clustering
Markov Clustering
0.596
I=3.5321321321321326
Clustering
Transitivity Clustering
0.732
T=0.6972111196383823
Clustering