Abstract
MOTIVATION: As data sets increase in size and complexity with advancing technology, flexible and interpretable data reduction methods that quantify information preservation become increasingly important. RESULTS: Super Partition is a large-scale approximation of the original Partition data reduction algorithm that allows the user to flexibly specify the minimum amount of information captured for each input feature. In an initial step, Genie, a fast, hierarchical clustering algorithm, forms a super-partition, thereby increasing the computational tractability by allowing Partition to be applied to the subsets. Applications to high dimensional data sets show scalability to hundreds of thousands of features with reasonable computation times. AVAILABILITY AND IMPLEMENTATION: Super Partition is a new function within the partition R package, available on the CRAN repository (https://cran.r-project.org/web/packages/partition/index.html).