ArticlePeerJ2025
Super Partition: fast, flexible, and interpretable large-scale data reduction in R.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
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).
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Registered trials
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