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=84
samples=20
Clustering
Self Organizing Maps 1.0 x=76
y=183
Clustering
Spectral Clustering 1.0 k=17 Clustering
clusterdp 1.0 k=3
dc=1.044321633166584
Clustering
HDBSCAN 1.0 minPts=12
k=238
Clustering
AGNES 1.0 method=average
metric=euclidean
k=149
Clustering
c-Means 1.0 k=174
m=3.5
Clustering
k-Medoids (PAM) 1.0 k=226 Clustering
DIANA 1.0 metric=euclidean
k=1
Clustering
DBSCAN 1.0 eps=2.741344287062283
MinPts=191
Clustering
Hierarchical Clustering 1.0 method=single
k=229
Clustering
fanny 1.0 k=30
membexp=1.1
Clustering
k-Means 1.0 k=220
nstart=10
Clustering
DensityCut 1.0 alpha=0.9486607142857143
K=4
Clustering
clusterONE 0.0 s=25
d=0.0
Clustering
Affinity Propagation 1.0 dampfact=0.99
preference=3.9162061243746904
maxits=3500
convits=200
Clustering
Markov Clustering 0.0 I=3.3361361361361364 Clustering
Transitivity Clustering 1.0 T=2.6931267341795917 Clustering
MCODE 0.999 v=0.9
cutoff=3.589855614010133
haircut=F
fluff=F
Clustering