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 0.839 metric=euclidean
k=7
samples=20
Clustering
Self Organizing Maps 0.741 x=2
y=2
Clustering
Spectral Clustering 0.944 k=2 Clustering
clusterdp 0.948 k=3
dc=0.06101759683428935
Clustering
HDBSCAN 0.737 minPts=2
k=5
Clustering
AGNES 0.893 method=complete
metric=euclidean
k=3
Clustering
c-Means 0.767 k=3
m=3.5
Clustering
k-Medoids (PAM) 0.896 k=5 Clustering
DIANA 0.852 metric=euclidean
k=20
Clustering
DBSCAN 0.601 eps=0.0
MinPts=4
Clustering
Hierarchical Clustering 1.0 method=complete
k=4
Clustering
fanny 0.944 k=2
membexp=2.0
Clustering
k-Means 0.944 k=8
nstart=10
Clustering
DensityCut 0.852 alpha=0.43333333333333335
K=2
Clustering
clusterONE 0.601 s=1
d=0.3
Clustering
Affinity Propagation 1.0 dampfact=0.7725
preference=0.0
maxits=2000
convits=350
Clustering
Markov Clustering 0.601 I=1.2247247247247248 Clustering
Transitivity Clustering 0.964 T=0.26324909144723435 Clustering
MCODE 0.601 v=0.0
cutoff=0.0
haircut=T
fluff=T
Clustering