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 0.736 metric=euclidean
k=1
samples=20
Clustering
Self Organizing Maps 0.742 x=2
y=1
Clustering
Spectral Clustering 0.81 k=4 Clustering
clusterdp 1.0 k=24
dc=1.4758472143145445
Clustering
HDBSCAN 0.958 minPts=2
k=12
Clustering
AGNES 0.958 method=weighted
metric=euclidean
k=10
Clustering
c-Means 0.759 k=2
m=1.5
Clustering
k-Medoids (PAM) 0.753 k=2 Clustering
DIANA 0.759 metric=euclidean
k=2
Clustering
DBSCAN 0.979 eps=12.790675857392719
MinPts=184
Clustering
Hierarchical Clustering 0.958 method=single
k=12
Clustering
fanny 0.776 k=3
membexp=2.0
Clustering
k-Means 0.736 k=2
nstart=10
Clustering
DensityCut 1.0 alpha=0.5401785714285714
K=13
Clustering
clusterONE 0.732 s=24
d=0.7333333333333333
Clustering
Affinity Propagation 0.732 dampfact=0.99
preference=3.689618035786361
maxits=2000
convits=500
Clustering
Markov Clustering 0.732 I=3.3539539539539542 Clustering
Transitivity Clustering 0.757 T=8.228697681413424 Clustering
MCODE 0.51 v=0.0
cutoff=13.52859946454999
haircut=T
fluff=T
Clustering