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	0.822	k=4	Clustering
clusterdp	0.992	k=25 dc=0.9969689222785935	Clustering
AGNES	1.0	method=average metric=euclidean k=2	Clustering
c-Means	0.862	k=2 m=1.01	Clustering
k-Medoids (PAM)	0.534	k=2	Clustering
DIANA	1.0	metric=euclidean k=6	Clustering
DBSCAN	1.0	eps=0.9636270673431192 MinPts=294	Clustering
Hierarchical Clustering	0.999	method=average k=2	Clustering
fanny	1.0	k=29 membexp=2.0	Clustering
clusterONE	1.0	s=308 d=0.5	Clustering
Affinity Propagation	0.684	dampfact=0.9175 preference=0.9090676683577977 maxits=5000 convits=425	Clustering
Markov Clustering	1.0	I=7.024424424424424	Clustering
Transitivity Clustering	1.0	T=0.9120714390727054	Clustering
MCODE	0.63	v=0.0 cutoff=0.9962111528482418 haircut=F fluff=F	Clustering