Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster
  Analysis
Tendai Mapungwana Chikake Boris Goldengorin
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.