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=1
samples=20
Clustering
Self Organizing Maps 0.987 x=2
y=1
Clustering
Spectral Clustering 1.0 k=12 Clustering
clusterdp 1.0 k=8
dc=9.760285629017215
Clustering
HDBSCAN 1.0 minPts=428
k=29
Clustering
AGNES 1.0 method=complete
metric=euclidean
k=6
Clustering
c-Means 1.0 k=2
m=1.5
Clustering
k-Medoids (PAM) 1.0 k=2 Clustering
DIANA 1.0 metric=euclidean
k=22
Clustering
DBSCAN 1.0 eps=3.7182040491494153
MinPts=60
Clustering
Hierarchical Clustering 1.0 method=average
k=2
Clustering
fanny 1.0 k=2
membexp=5.0
Clustering
k-Means 1.0 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.4309523809523809
K=107
Clustering
clusterONE 1.0 s=540
d=0.43333333333333335
Clustering
Affinity Propagation 1.0 dampfact=0.845
preference=0.0
maxits=2750
convits=500
Clustering
Markov Clustering 1.0 I=3.7281281281281284 Clustering
Transitivity Clustering 1.0 T=9.560697348601161 Clustering
MCODE 0.909 v=0.5
cutoff=12.781326418951116
haircut=T
fluff=T
Clustering