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=complete metric=euclidean k=198
chang_pathbased	1.0	method=average metric=euclidean k=227
ppi_mips	1.0	method=flexible metric=euclidean k=1095
chang_spiral	1.0	method=complete metric=euclidean k=251
astral_40_strsim	1.0	method=flexible metric=euclidean k=895
astral_40_seqsim_beh	1.0	method=average metric=euclidean k=702
fraenti_s3	1.0	method=ward metric=euclidean k=2134
bone_marrow_fixLabels	0.987	method=average metric=euclidean k=11
fu_flame	1.0	method=ward metric=euclidean k=8
coli_state	1.0	method=ward metric=euclidean k=190
coli_find	1.0	method=single metric=euclidean k=420
coli_need	1.0	method=ward metric=euclidean k=103
coli_time	1.0	method=complete metric=euclidean k=511
gionis_aggregation	1.0	method=single metric=euclidean k=644
veenman_r15	1.0	method=single metric=euclidean k=600
zahn_compound	1.0	method=flexible metric=euclidean k=272
synthetic_spirals	1.0	method=ward metric=euclidean k=198
synthetic_cassini	1.0	method=ward metric=euclidean k=43
twonorm_100d	1.0	method=single metric=euclidean k=200
twonorm_50d	1.0	method=ward metric=euclidean k=197
synthetic_cuboid	1.0	method=average metric=euclidean k=116
astral1_161	1.0	method=complete metric=euclidean k=123
tcga	1.0	method=average metric=euclidean k=29
bone_marrow	1.0	method=single metric=euclidean k=23
zachary	1.0	method=complete metric=euclidean k=32