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 0.0 metric=euclidean
k=109
samples=20
Clustering
Self Organizing Maps 0.0 x=35
y=59
Clustering
Spectral Clustering 0.0 k=17 Clustering
clusterdp 0.0 k=20
dc=3.1329648994997523
Clustering
HDBSCAN 0.0 minPts=36
k=155
Clustering
AGNES 0.0 method=single
metric=euclidean
k=101
Clustering
c-Means 0.0 k=52
m=5.0
Clustering
k-Medoids (PAM) 0.0 k=5 Clustering
DIANA 0.0 metric=euclidean
k=58
Clustering
DBSCAN 0.0 eps=1.8275628580415222
MinPts=200
Clustering
Hierarchical Clustering 0.0 method=average
k=169
Clustering
fanny 0.0 k=72
membexp=5.0
Clustering
k-Means 0.0 k=129
nstart=10
Clustering
DensityCut 0.0 alpha=0.93798828125
K=6
Clustering
clusterONE 1.0 s=233
d=0.43333333333333335
Clustering
Affinity Propagation 0.0 dampfact=0.845
preference=0.9790515310936726
maxits=2000
convits=500
Clustering
Markov Clustering 1.0 I=2.365065065065065 Clustering
Transitivity Clustering 0.0 T=2.73624812293647 Clustering
MCODE 0.001 v=0.8
cutoff=3.589855614010133
haircut=F
fluff=F
Clustering