localbins
Localbins is a data discretization or histogram construction approach in which bin boundaries are determined by the local distribution of the data rather than fixed-width intervals. The goal is to create bins that reflect the density and structure of the data, producing a more informative representation for analysis and visualization.
Variants of localbins include density-based binning, equal-density or equal-frequency binning, and neighbor-driven binning. Density-based methods use
Typical workflow involves estimating local structure (density or neighbor counts), determining bin edges to meet a
Applications of localbins include data visualization for skewed distributions, feature discretization for machine learning algorithms, and
See also: histogram, adaptive histogram, equal-frequency binning, density estimation, discretization.