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 an estimate of the data density, such as kernel density estimation, to place edges where the density changes meaningfully. Equal-frequency variants partition the domain so that each bin contains roughly the same number of observations, adapting to regions of high and low density. Neighbor-driven approaches set bin widths based on the distance to nearby points (for example, targeting a fixed number of samples per bin).

Typical workflow involves estimating local structure (density or neighbor counts), determining bin edges to meet a chosen criterion (mass, density change, or sample count), and applying the resulting bins to the dataset. The method can be used to discretize continuous features, produce histograms with adaptive resolution, or preprocess data for models that benefit from categorical inputs.

Applications of localbins include data visualization for skewed distributions, feature discretization for machine learning algorithms, and preprocessing steps in signal or image processing where local structure needs preservation. Advantages include better preservation of local patterns and reduced information loss in dense regions; disadvantages include sensitivity to parameter choices (bandwidth, target counts) and higher computational requirements compared to fixed-bin methods.

See also: histogram, adaptive histogram, equal-frequency binning, density estimation, discretization.