ClustEval | Which parameter sets lead to the optimal clustering quality?

ClustEval clustering evaluation framework

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Which parameter sets lead to the optimal clustering quality?

Please choose a clustering quality measure:

Program	Best quality	Parameter set	Clustering
Spectral Clustering	0.998	k=2	Clustering
clusterdp	0.994	k=8 dc=0.8719264318015347	Clustering
AGNES	1.0	method=weighted metric=euclidean k=1	Clustering
c-Means	0.921	k=2 m=1.5	Clustering
k-Medoids (PAM)	0.794	k=2	Clustering
DIANA	1.0	metric=euclidean k=1	Clustering
DBSCAN	1.0	eps=0.9957308810600514 MinPts=511	Clustering
Hierarchical Clustering	0.994	method=average k=2	Clustering
fanny	1.0	k=3 membexp=1.1	Clustering
clusterONE	1.0	s=358 d=0.7333333333333333	Clustering
Affinity Propagation	0.395	dampfact=0.7725 preference=0.8719264318015347 maxits=4250 convits=350	Clustering
Markov Clustering	1.0	I=7.077877877877879	Clustering
Transitivity Clustering	1.0	T=0.9108997698719387	Clustering
MCODE	0.574	v=0.2 cutoff=0.9946636013250642 haircut=F fluff=F	Clustering