Clusteval logo ClustEval clustering evaluation framework

Which parameter sets lead to the optimal clustering quality?

Please choose a clustering quality measure:
Program Best quality Parameter set Clustering
CLARA 0.701 metric=euclidean
k=28
samples=20
Clustering
Self Organizing Maps 0.696 x=105
y=52
Clustering
Spectral Clustering 0.952 k=3 Clustering
clusterdp 1.0 k=3
dc=4.041033971096452
Clustering
HDBSCAN 0.938 minPts=5
k=34
Clustering
AGNES 1.0 method=weighted
metric=euclidean
k=3
Clustering
c-Means 0.705 k=26
m=2.25
Clustering
k-Medoids (PAM) 0.705 k=26 Clustering
DIANA 0.699 metric=euclidean
k=37
Clustering
DBSCAN 1.0 eps=14.14361889883758
MinPts=280
Clustering
Hierarchical Clustering 1.0 method=complete
k=3
Clustering
fanny 0.705 k=26
membexp=5.0
Clustering
k-Means 0.7 k=30
nstart=10
Clustering
DensityCut 1.0 alpha=0.2777777777777778
K=7
Clustering
clusterONE 0.331 s=1
d=0.0
Clustering
Affinity Propagation 0.702 dampfact=0.7725
preference=7.576938695805847
maxits=2000
convits=425
Clustering
Markov Clustering 0.331 I=1.1712712712712714 Clustering
Transitivity Clustering 0.72 T=25.423321830171368 Clustering
MCODE 0.698 v=0.4
cutoff=26.519285435320466
haircut=T
fluff=T
Clustering