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=420	Clustering
clusterdp	0.124	k=25 dc=0.9120987460792045	Clustering
AGNES	1.0	method=single metric=euclidean k=420	Clustering
c-Means	1.0	k=90 m=5.0	Clustering
k-Medoids (PAM)	1.0	k=419	Clustering
DIANA	1.0	metric=euclidean k=419	Clustering
DBSCAN	0.0	eps=0.9363473678504585 MinPts=28	Clustering
Hierarchical Clustering	1.0	method=complete k=420	Clustering
fanny	1.0	k=139 membexp=1.1	Clustering
clusterONE	0.0	s=294 d=0.7333333333333333	Clustering
Affinity Propagation	1.0	dampfact=0.845 preference=1.0000000000000002 maxits=4250 convits=200	Clustering
Markov Clustering	0.0	I=9.93763763763764	Clustering
Transitivity Clustering	1.0	T=1.0000000000000002	Clustering
MCODE	0.734	v=0.8 cutoff=0.9962111528482418 haircut=T fluff=T	Clustering