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=4 Clustering
clusterdp 1.0 k=5
dc=33.64006605885971
Clustering
HDBSCAN 1.0 minPts=289
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=47
Clustering
DBSCAN 1.0 eps=11.644638251143743
MinPts=761
Clustering
Hierarchical Clustering 1.0 method=average
k=9
Clustering
fanny 1.0 k=6
membexp=2.0
Clustering
k-Means 0.993 k=3
nstart=10
Clustering
DensityCut 1.0 alpha=0.23333333333333334
K=109
Clustering
clusterONE 1.0 s=525
d=0.5666666666666667
Clustering
Affinity Propagation 1.0 dampfact=0.7
preference=0.0
maxits=2000
convits=200
Clustering
Markov Clustering 1.0 I=9.821821821821821 Clustering
Transitivity Clustering 1.0 T=15.541726060919245 Clustering
MCODE 0.818 v=0.0
cutoff=35.58083910071699
haircut=F
fluff=F
Clustering