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
-0.042
k=34
Clustering
clusterdp
0.161
k=19
dc=3.966666666666667
Clustering
AGNES
0.261
method=weighted
metric=euclidean
k=19
Clustering
c-Means
0.156
k=11
m=5.0
Clustering
k-Medoids (PAM)
0.139
k=17
Clustering
DIANA
0.214
metric=euclidean
k=22
Clustering
DBSCAN
0.118
eps=7.0
MinPts=7
Clustering
Hierarchical Clustering
0.252
method=single
k=19
Clustering
fanny
0.152
k=8
membexp=2.0
Clustering
clusterONE
0.142
s=9
d=0.4666666666666667
Clustering
Affinity Propagation
0.137
dampfact=0.9175
preference=1.75
maxits=5000
convits=425
Clustering
Markov Clustering
0.146
I=2.4452452452452453
Clustering
Transitivity Clustering
0.242
T=2.5085085085085086
Clustering