Clust
Eval
clustering evaluation framework
Welcome
Overview
Clustering Methods
Data Sets
Measures
Submit
Advanced
Help
About us
Location:
Clustering Methods
»
fanny
»
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
0.872
k=21
membexp=2.0
chang_pathbased
0.829
k=44
membexp=1.1
ppi_mips
0.997
k=528
membexp=1.1
chang_spiral
0.705
k=26
membexp=5.0
astral_40_strsim
0.993
k=219
membexp=2.0
astral_40_seqsim_beh
0.989
k=505
membexp=5.0
fraenti_s3
0.964
k=17
membexp=2.0
bone_marrow_fixLabels
0.959
k=2
membexp=2.0
fu_flame
0.773
k=4
membexp=5.0
coli_state
0.614
k=26
membexp=5.0
coli_find
0.873
k=134
membexp=2.0
coli_need
0.613
k=49
membexp=1.1
coli_time
0.736
k=46
membexp=5.0
gionis_aggregation
0.932
k=4
membexp=5.0
veenman_r15
0.998
k=18
membexp=2.0
zahn_compound
0.909
k=17
membexp=5.0
synthetic_spirals
0.515
k=22
membexp=1.1
synthetic_cassini
0.951
k=3
membexp=1.1
twonorm_100d
0.819
k=2
membexp=1.3966666666666667
twonorm_50d
0.951
k=2
membexp=1.99
synthetic_cuboid
1.0
k=6
membexp=1.1
astral1_161
0.862
k=46
membexp=2.0
tcga
0.908
k=2
membexp=5.0
bone_marrow
0.9
k=3
membexp=1.1
zachary
1.0
k=2
membexp=2.0