Dimensionality Reduction Using Pseudo-Boolean Polynomials for Cluster Analysis

Tendai Mapungwana Chikake () and Boris Goldengorin ()
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Tendai Mapungwana Chikake: Moscow Institute of Physics and Technology
Boris Goldengorin: New Uzbekistan University

A chapter in Data Analysis and Optimization, 2023, pp 59-72 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-31654-8_4

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DOI: 10.1007/978-3-031-31654-8_4

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