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
0.691
k=2
Clustering
clusterdp
0.97
k=3
dc=3.966666666666667
Clustering
AGNES
1.0
method=flexible
metric=euclidean
k=1
Clustering
c-Means
1.0
k=2
m=3.5
Clustering
k-Medoids (PAM)
0.94
k=3
Clustering
DIANA
0.834
metric=euclidean
k=17
Clustering
DBSCAN
0.834
eps=0.0
MinPts=3
Clustering
Hierarchical Clustering
1.0
method=average
k=2
Clustering
fanny
1.0
k=2
membexp=2.0
Clustering
clusterONE
0.847
s=6
d=0.13333333333333333
Clustering
Affinity Propagation
0.612
dampfact=0.7725
preference=1.75
maxits=4250
convits=500
Clustering
Markov Clustering
1.0
I=1.50980980980981
Clustering
Transitivity Clustering
1.0
T=0.11211211211211211
Clustering