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	0.998	k=2	Clustering
clusterdp	0.994	k=25 dc=0.8847337886213812	Clustering
AGNES	1.0	method=complete metric=euclidean k=1	Clustering
c-Means	0.921	k=3 m=3.5	Clustering
k-Medoids (PAM)	0.794	k=2	Clustering
DIANA	1.0	metric=euclidean k=3	Clustering
DBSCAN	1.0	eps=0.9274249780208698 MinPts=120	Clustering
Hierarchical Clustering	0.994	method=average k=2	Clustering
fanny	1.0	k=3 membexp=1.1	Clustering
clusterONE	1.0	s=307 d=0.13333333333333333	Clustering
Affinity Propagation	0.395	dampfact=0.7725 preference=0.9039448238511512 maxits=2000 convits=425	Clustering
Markov Clustering	1.0	I=6.062262262262263	Clustering
Transitivity Clustering	1.0	T=0.9151304282808969	Clustering
MCODE	0.574	v=0.2 cutoff=0.9946636013250642 haircut=F fluff=F	Clustering