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Super Partition: fast, flexible, and interpretable large-scale data reduction in R.

Katelyn J Queen1, Malcolm Barrett2, Joshua Millstein1

  • 1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, United States.

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Super Partition offers a scalable data reduction method for large, complex datasets. This approximation of the Partition algorithm enhances computational tractability for high-dimensional data analysis.

Keywords:
Big dataClusteringData reduction

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Area of Science:

  • Data Science
  • Computational Statistics

Background:

  • Increasing data size and complexity necessitate advanced data reduction techniques.
  • Existing methods require flexible information preservation quantification.

Purpose of the Study:

  • Introduce Super Partition, a scalable approximation of the Partition algorithm.
  • Enable flexible specification of minimum information capture per feature.

Main Methods:

  • Utilize Genie, a fast hierarchical clustering algorithm, for initial super-partition formation.
  • Apply the Partition algorithm to generated subsets for computational tractability.

Main Results:

  • Demonstrate scalability to hundreds of thousands of features for high-dimensional datasets.
  • Achieve reasonable computation times for large-scale data reduction.

Conclusions:

  • Super Partition provides an efficient and flexible approach to data reduction.
  • The method is suitable for handling complex and high-dimensional datasets effectively.