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 0.667 metric=euclidean
k=1
samples=20		 Clustering
Self Organizing Maps 0.666 x=2
y=1		 Clustering
Spectral Clustering 0.965 k=4 Clustering
clusterdp 0.666 k=2
dc=0.0		 Clustering
HDBSCAN 0.667 minPts=29
k=1		 Clustering
AGNES 0.89 method=flexible
metric=euclidean
k=2		 Clustering
c-Means 0.97 k=2
m=3.5		 Clustering
k-Medoids (PAM) 0.66 k=3 Clustering
DIANA 0.95 metric=euclidean
k=47		 Clustering
DBSCAN 0.667 eps=0.0
MinPts=93		 Clustering
Hierarchical Clustering 0.89 method=single
k=2		 Clustering
fanny 0.899 k=2
membexp=1.3966666666666667		 Clustering
k-Means 0.965 k=2
nstart=10		 Clustering
DensityCut 0.667 alpha=0.8095238095238095
K=29		 Clustering
clusterONE 0.788 s=13
d=0.2		 Clustering
Markov Clustering 0.667 I=1.4741741741741743 Clustering
Transitivity Clustering 0.667 T=0.12759796951093313 Clustering
MCODE 0.667 v=0.8
cutoff=7.587522115560845
haircut=F
fluff=T		 Clustering