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	0.0	k=1046	Clustering
clusterdp	0.0	k=560 dc=21.834136	Clustering
AGNES	0.0	method=average metric=euclidean k=525	Clustering
c-Means	0.0	k=201 m=5.0	Clustering
k-Medoids (PAM)	0.0	k=912	Clustering
DIANA	0.0	metric=euclidean k=614	Clustering
DBSCAN	0.0	eps=8.7336544 MinPts=733	Clustering
Hierarchical Clustering	0.0	method=average k=966	Clustering
fanny	0.0	k=339 membexp=5.0	Clustering
clusterONE	1.0	s=594 d=0.03333333333333333	Clustering
Affinity Propagation	0.0	dampfact=0.845 preference=43.668272 maxits=2750 convits=200	Clustering
Markov Clustering	0.001	I=9.928728728728728	Clustering
Transitivity Clustering	0.0	T=38.772529793793794	Clustering
MCODE	0.024	v=0.4 cutoff=3.639022666666667 haircut=F fluff=T	Clustering