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 0.294 metric=euclidean
k=58
samples=20
Clustering
Self Organizing Maps 0.271 x=134
y=100
Clustering
Spectral Clustering 0.667 k=2 Clustering
clusterdp 1.0 k=5
dc=1.3248
Clustering
HDBSCAN 1.0 minPts=6
k=2
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=2
Clustering
c-Means 0.3 k=52
m=1.5
Clustering
k-Medoids (PAM) 0.305 k=45 Clustering
DIANA 0.282 metric=euclidean
k=75
Clustering
DBSCAN 1.0 eps=1.5456
MinPts=233
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 0.274 k=84
membexp=5.0
Clustering
k-Means 0.291 k=61
nstart=10
Clustering
DensityCut 1.0 alpha=0.0357142834525023
K=4
Clustering
clusterONE 0.0 s=9
d=0.26666666666666666
Clustering
Affinity Propagation 0.285 dampfact=0.99
preference=2.484
maxits=4250
convits=350
Clustering
Markov Clustering 0.0 I=8.432032032032032 Clustering
Transitivity Clustering 0.312 T=2.9473153153153158 Clustering
MCODE 0.288 v=0.6
cutoff=3.036
haircut=T
fluff=T
Clustering