Clusteval logo ClustEval clustering evaluation framework
Location: Data Sets » twonorm_100d » Best Parameters
Navigation:
027Hints:

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=197
samples=20		 Clustering
Self Organizing Maps 0.0 x=200
y=160		 Clustering
Spectral Clustering 0.005 k=110 Clustering
clusterdp 0.043 k=3
dc=4.856014153958941		 Clustering
HDBSCAN 0.0 minPts=76
k=200		 Clustering
AGNES 0.0 method=weighted
metric=euclidean
k=200		 Clustering
c-Means 0.0 k=26
m=5.0		 Clustering
k-Medoids (PAM) 0.0 k=195 Clustering
DIANA 0.0 metric=euclidean
k=199		 Clustering
DBSCAN 0.0 eps=10.319030077162749
MinPts=60		 Clustering
Hierarchical Clustering 0.0 method=single
k=199		 Clustering
fanny 0.0 k=61
membexp=2.2866666666666666		 Clustering
k-Means 0.0 k=199
nstart=10		 Clustering
DensityCut 0.733 alpha=0.4583333333333333
K=3		 Clustering
clusterONE 0.02 s=5
d=0.5666666666666667		 Clustering
Markov Clustering 1.0 I=2.2670670670670674 Clustering
Transitivity Clustering 0.0 T=16.09557243973628 Clustering
MCODE 0.063 v=0.2
cutoff=4.552513269336507
haircut=T
fluff=F		 Clustering