Description Usage Value See Also
View source: R/machine_learning_benchmark.R
GetClusteringDefaultMicrobenchmarks
defines the default clustering
microbenchmarks to be executed by the
RunMachineLearningBenchmark
function. The current clustering
microbenchmarks are:
pam_cluster_3_7_2500N=3, seven clusters with 2500 vectors per cluster, using pam function
pam_cluster_3_7_5000N=3, seven clusters with 5000 vectors per cluster, using pam function
pam_cluster_3_7_5715N=3, seven clusters with 5715 vectors per cluster, using pam function
pam_cluster_16_33_1213N=16, 33 clusters with 1213 vectors per cluster, using pam function
pam_cluster_64_33_1213N=64, 33 clusters with 1213 vectors per cluster, using pam function
pam_cluster_16_7_2858N=16, seven clusters with 2858 vectors per cluster, using pam function
pam_cluster_32_7_2858N=32, seven clusters with 2858 vectors per cluster, using pam function
pam_cluster_64_7_5715,N=64, seven clusters with 5715 vectors per cluster, using pam function
clara_cluster_64_33_1213N=64, 33 clusters with 1213 vectors per cluster, using clara function
clara_cluster_1000_99_1000N=1000, 99 clusters with 1000 vectors per cluster, using clara function
The pam
and pam
microbenchmarks test those clustering functions. The pam function
applies a quadratic time algorithm to partition around medoids (pam); the
clara function is a linear time approximation to the partitioning around
medoids algorithm. See the documentation for the
ClusteringMicrobenchmark
class for more details.
1 |
a list of ClusteringMicrobenchmark
objects defining the
microbenchmarks to be executed. The microbenchmarks appear in the order
listed in the function description and are assigned the names enumerated
in the description.
ClusteringMicrobenchmark
pam
pam
Other machine learning default microbenchmarks: GetClusteringExampleMicrobenchmarks
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