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=2
samples=20
Clustering
Self Organizing Maps 0.717 x=2
y=1
Clustering
Spectral Clustering 0.665 k=4 Clustering
clusterdp 1.0 k=24
dc=1.4758472143145445
Clustering
HDBSCAN 1.0 minPts=24
k=1
Clustering
AGNES 1.0 method=ward
metric=euclidean
k=1
Clustering
c-Means 0.732 k=2
m=1.5
Clustering
k-Medoids (PAM) 0.726 k=2 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=0.0
MinPts=72
Clustering
Hierarchical Clustering 0.989 method=average
k=2
Clustering
fanny 1.0 k=6
membexp=1.1
Clustering
k-Means 0.711 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.5714285714285714
K=12
Clustering
clusterONE 1.0 s=1
d=0.3333333333333333
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=3.689618035786361
maxits=2000
convits=500
Clustering
Markov Clustering 1.0 I=1.1356356356356356 Clustering
Transitivity Clustering 1.0 T=0.014773245388533977 Clustering
MCODE 0.499 v=0.0
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering