GetClusteringDefaultMicrobenchmarks {RHPCBenchmark}

Initializes the list of default clustering microbenchmarks

Description

GetClusteringDefaultMicrobenchmarks defines the default clustering microbenchmarks to be executed by the RunMachineLearningBenchmark function. The current clustering microbenchmarks are:

1. pam_cluster_3_7_2500N=3, seven clusters with 2500 vectors per cluster, using pam function

2. pam_cluster_3_7_5000N=3, seven clusters with 5000 vectors per cluster, using pam function

3. pam_cluster_3_7_5715N=3, seven clusters with 5715 vectors per cluster, using pam function

4. pam_cluster_16_33_1213N=16, 33 clusters with 1213 vectors per cluster, using pam function

5. pam_cluster_64_33_1213N=64, 33 clusters with 1213 vectors per cluster, using pam function

6. pam_cluster_16_7_2858N=16, seven clusters with 2858 vectors per cluster, using pam function

7. pam_cluster_32_7_2858N=32, seven clusters with 2858 vectors per cluster, using pam function

8. pam_cluster_64_7_5715,N=64, seven clusters with 5715 vectors per cluster, using pam function

9. clara_cluster_64_33_1213N=64, 33 clusters with 1213 vectors per cluster, using clara function

10. 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.

Usage

GetClusteringDefaultMicrobenchmarks()

Value

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.

See Also

ClusteringMicrobenchmark pam pam

Other machine learning default microbenchmarks: GetClusteringExampleMicrobenchmarks