Clust
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clustering evaluation framework
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ppi_mips
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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
0.0
k=1094
Clustering
clusterdp
0.0
k=38
dc=0.3
Clustering
AGNES
0.0
method=single
metric=euclidean
k=1095
Clustering
c-Means
0.0
k=913
m=1.5
Clustering
k-Medoids (PAM)
0.0
k=1054
Clustering
DIANA
0.0
metric=euclidean
k=1080
Clustering
DBSCAN
0.003
eps=0.5
MinPts=729
Clustering
Hierarchical Clustering
0.0
method=average
k=1087
Clustering
fanny
0.0
k=245
membexp=1.1
Clustering
clusterONE
0.004
s=1
d=0.8333333333333334
Clustering
Affinity Propagation
0.567
dampfact=0.7
preference=0.25
maxits=2000
convits=200
Clustering
Markov Clustering
0.007
I=4.52992992992993
Clustering
Transitivity Clustering
0.0
T=1.0
Clustering