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Dimensionality Reduction Using Pseudo-Boolean Polynomials for Cluster Analysis

In: Data Analysis and Optimization

Author

Listed:
  • Tendai Mapungwana Chikake

    (Moscow Institute of Physics and Technology)

  • Boris Goldengorin

    (New Uzbekistan University
    Pskov State University
    Moscow Institute of Physics and Technology, Dolgoprudny)

Abstract

We introduce usage of a reduction property of penalty-based formulation of pseudo-Boolean polynomials as a mechanism for invariant dimensionality reduction in cluster analysis processes. In our experiments, we show that multidimensional data, like 4-dimensional Iris Flower dataset can be reduced to 2-dimensional space while the 30-dimensional Wisconsin Diagnostic Breast Cancer (WDBC) dataset can be reduced to 3-dimensional space, and by searching lines or planes that lie between reduced samples we can extract clusters in a linear and unbiased manner with competitive accuracies, reproducibility and clear interpretation.

Suggested Citation

  • Tendai Mapungwana Chikake & Boris Goldengorin, 2023. "Dimensionality Reduction Using Pseudo-Boolean Polynomials for Cluster Analysis," Springer Optimization and Its Applications, in: Boris Goldengorin & Sergei Kuznetsov (ed.), Data Analysis and Optimization, pages 59-72, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-31654-8_4
    DOI: 10.1007/978-3-031-31654-8_4
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