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 0.667 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.504 x=2
y=1
Clustering
Spectral Clustering 0.698 k=2 Clustering
clusterdp 1.0 k=5
dc=0.22080000000000002
Clustering
HDBSCAN 1.0 minPts=6
k=2
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=2
Clustering
c-Means 0.504 k=2
m=1.5
Clustering
k-Medoids (PAM) 0.504 k=2 Clustering
DIANA 0.667 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=3.0912
MinPts=175
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.667 k=2
membexp=2.0
Clustering
k-Means 0.502 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.03512137276785712
K=4
Clustering
clusterONE 0.667 s=67
d=0.6333333333333333
Clustering
Affinity Propagation 0.667 dampfact=0.99
preference=0.8280000000000001
maxits=2000
convits=350
Clustering
Markov Clustering 0.667 I=5.011011011011011 Clustering
Transitivity Clustering 0.667 T=0.6730090090090091 Clustering
MCODE 0.637 v=0.3
cutoff=1.2420000000000002
haircut=F
fluff=F
Clustering