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 1.0 metric=euclidean
k=18
samples=20
Clustering
Self Organizing Maps 0.979 x=2
y=27
Clustering
Spectral Clustering 1.0 k=5 Clustering
clusterdp 1.0 k=4
dc=1.293848694571527
Clustering
HDBSCAN 1.0 minPts=375
k=1
Clustering
AGNES 1.0 method=single
metric=euclidean
k=4
Clustering
c-Means 0.993 k=3
m=1.5
Clustering
k-Medoids (PAM) 0.995 k=2 Clustering
DIANA 1.0 metric=euclidean
k=2
Clustering
DBSCAN 1.0 eps=6.469243472857636
MinPts=578
Clustering
Hierarchical Clustering 1.0 method=complete
k=6
Clustering
fanny 1.0 k=6
membexp=2.0
Clustering
k-Means 0.993 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.13333333333333333
K=109
Clustering
clusterONE 1.0 s=368
d=0.3
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.0
maxits=2750
convits=500
Clustering
Markov Clustering 1.0 I=10.0 Clustering
Transitivity Clustering 1.0 T=1.9427157576149057 Clustering
MCODE 0.818 v=0.0
cutoff=35.58083910071699
haircut=T
fluff=F
Clustering