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=29.112181333333336	Clustering
AGNES	0.0	method=ward metric=euclidean k=870	Clustering
c-Means	0.0	k=911 m=3.5	Clustering
k-Medoids (PAM)	0.0	k=924	Clustering
DIANA	0.0	metric=euclidean k=603	Clustering
DBSCAN	0.0	eps=0.0 MinPts=1	Clustering
Hierarchical Clustering	0.0	method=single k=843	Clustering
fanny	0.0	k=213 membexp=1.1	Clustering
clusterONE	1.0	s=873 d=0.8	Clustering
Affinity Propagation	0.0	dampfact=0.9175 preference=43.668272 maxits=5000 convits=500	Clustering
Markov Clustering	0.001	I=9.93763763763764	Clustering
Transitivity Clustering	0.0	T=10.053756316316315	Clustering
MCODE	0.024	v=0.4 cutoff=3.639022666666667 haircut=F fluff=F	Clustering