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:

Dataset	Best quality	Parameter set
brown	1.0	method=ward metric=euclidean k=6
chang_pathbased	1.0	method=single metric=euclidean k=3
ppi_mips	1.0	method=ward metric=euclidean k=1
chang_spiral	1.0	method=single metric=euclidean k=1
astral_40_strsim	1.0	method=complete metric=euclidean k=18
astral_40_seqsim_beh	1.0	method=average metric=euclidean k=13
fraenti_s3	1.0	method=weighted metric=euclidean k=1
bone_marrow_fixLabels	1.0	method=weighted metric=euclidean k=1
fu_flame	1.0	method=flexible metric=euclidean k=1
coli_state	1.0	method=flexible metric=euclidean k=1
coli_find	1.0	method=weighted metric=euclidean k=1
coli_need	1.0	method=ward metric=euclidean k=1
coli_time	1.0	method=ward metric=euclidean k=1
gionis_aggregation	1.0	method=flexible metric=euclidean k=3
veenman_r15	1.0	method=average metric=euclidean k=17
zahn_compound	1.0	method=single metric=euclidean k=1
synthetic_spirals	1.0	method=ward metric=euclidean k=1
synthetic_cassini	1.0	method=average metric=euclidean k=2
twonorm_100d	1.0	method=flexible metric=euclidean k=1
twonorm_50d	1.0	method=ward metric=euclidean k=1
synthetic_cuboid	1.0	method=flexible metric=euclidean k=3
astral1_161	1.0	method=average metric=euclidean k=5
tcga	1.0	method=single metric=euclidean k=1
bone_marrow	1.0	method=ward metric=euclidean k=1
zachary	1.0	method=flexible metric=euclidean k=1