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
0.997
k=28
Clustering
clusterdp
1.0
k=19
dc=0.7
Clustering
AGNES
1.0
method=single
metric=euclidean
k=6
Clustering
c-Means
1.0
k=11
m=1.5
Clustering
k-Medoids (PAM)
1.0
k=8
Clustering
DIANA
1.0
metric=euclidean
k=12
Clustering
DBSCAN
1.0
eps=0.0
MinPts=1
Clustering
Hierarchical Clustering
1.0
method=single
k=4
Clustering
fanny
1.0
k=12
membexp=1.1
Clustering
clusterONE
1.0
s=3
d=0.7666666666666667
Clustering
Affinity Propagation
1.0
dampfact=0.7725
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering
1.0
I=8.44984984984985
Clustering
Transitivity Clustering
1.0
T=5.4374374374374375
Clustering