Clust
Eval
clustering evaluation framework
Welcome
Overview
Clustering Methods
Data Sets
Measures
Submit
Advanced
Help
About us
Location:
Clustering Methods
»
Spectral Clustering
»
Best Parameters
Navigation:
General
Best Qualities
Best Parameters
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
Dataset
Best quality
Parameter set
brown
0.858
k=15
chang_pathbased
0.898
k=8
ppi_mips
0.985
k=103
chang_spiral
0.952
k=3
astral_40_strsim
0.989
k=1046
astral_40_seqsim_beh
0.989
k=932
fraenti_s3
0.966
k=42
bone_marrow_fixLabels
0.959
k=2
fu_flame
0.816
k=4
coli_state
0.609
k=123
coli_find
0.873
k=419
coli_need
0.614
k=103
coli_time
0.736
k=511
gionis_aggregation
0.976
k=8
veenman_r15
0.991
k=25
zahn_compound
0.883
k=20
synthetic_spirals
0.75
k=2
synthetic_cassini
1.0
k=18
twonorm_100d
0.932
k=4
twonorm_50d
0.942
k=2
synthetic_cuboid
0.843
k=22
astral1_161
0.898
k=3
tcga
0.499
k=5
bone_marrow
0.642
k=26
zachary
0.624
k=5