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	1.0	k=511	Clustering
clusterdp	0.089	k=24 dc=0.9743852863603072	Clustering
AGNES	1.0	method=weighted metric=euclidean k=511	Clustering
c-Means	1.0	k=4 m=1.01	Clustering
k-Medoids (PAM)	1.0	k=510	Clustering
DIANA	1.0	metric=euclidean k=511	Clustering
DBSCAN	0.0	eps=0.9018102643811766 MinPts=188	Clustering
Hierarchical Clustering	1.0	method=complete k=509	Clustering
fanny	1.0	k=244 membexp=5.0	Clustering
clusterONE	0.0	s=409 d=0.8333333333333334	Clustering
Affinity Propagation	1.0	dampfact=0.7725 preference=1.0000000000000002 maxits=5000 convits=500	Clustering
Markov Clustering	0.0	I=9.091291291291292	Clustering
Transitivity Clustering	1.0	T=0.9997435964600633	Clustering
MCODE	0.716	v=0.0 cutoff=0.9946636013250642 haircut=T fluff=F	Clustering