Clust
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clustering evaluation framework
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Best Parameters
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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
0.729
k=31
Clustering
clusterdp
0.321
k=4
dc=6.766666666666667
Clustering
AGNES
0.0
method=flexible
metric=euclidean
k=33
Clustering
c-Means
0.0
k=33
m=1.01
Clustering
k-Medoids (PAM)
0.0
k=32
Clustering
DIANA
0.0
metric=euclidean
k=31
Clustering
DBSCAN
0.637
eps=4.9
MinPts=32
Clustering
Hierarchical Clustering
0.0
method=single
k=33
Clustering
fanny
0.475
k=6
membexp=5.0
Clustering
clusterONE
0.837
s=4
d=0.7333333333333333
Clustering
Affinity Propagation
0.27
dampfact=0.9175
preference=7.0
maxits=2000
convits=200
Clustering
Markov Clustering
0.708
I=3.888488488488489
Clustering
Transitivity Clustering
0.0
T=6.187187187187187
Clustering