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=189	Clustering
clusterdp	0.33	k=25 dc=0.8916076774870505	Clustering
AGNES	1.0	method=average metric=euclidean k=157	Clustering
c-Means	1.0	k=188 m=2.25	Clustering
k-Medoids (PAM)	1.0	k=157	Clustering
DIANA	1.0	metric=euclidean k=190	Clustering
DBSCAN	0.0	eps=0.9317529821214764 MinPts=70	Clustering
Hierarchical Clustering	1.0	method=single k=165	Clustering
fanny	1.0	k=66 membexp=1.1	Clustering
clusterONE	0.0	s=152 d=0.7333333333333333	Clustering
Affinity Propagation	1.0	dampfact=0.99 preference=1.0000000000000002 maxits=5000 convits=500	Clustering
Markov Clustering	0.0	I=9.465465465465465	Clustering
Transitivity Clustering	1.0	T=0.999397217648132	Clustering
MCODE	0.789	v=0.9 cutoff=0.994981836920697 haircut=T fluff=F	Clustering