Cluster-Preserving Multivariate Joint Grid Discretization

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data (Wang et al 2020) . It can handle both labeled or unlabeled data. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

title: The 'GridOnClusters' R package bibliography: inst/REFERENCES.bib

Overview

The package offers a method to discretize multivariate continuous data using a grid that captures the joint distribution via preserving clusters in the original data (Wang, Kumar, and Song 2020). Joint grid discretization is applicable as a data transformation step before using other methods to infer association, function, or causality without assuming a parametric model.

When to use the package

Most available discretization methods process one variable at a time, such as 'Ckmeans.1d.dp'. If discretizing each variable independently misses patterns arising from the joint distribution of multiple involved variables, one may benefit from using the joint discretization method in this package.

To download and install the package

install.packages("GridOnClusters")

Examples

See the Examples vignette of the package.

Citing the package

Wang J, Kumar S, Song M (2020). "Joint Grid Discretization for Biological Pattern Discovery." In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. Article no. 57. doi: 10.1145/3388440.3412415 (URL: https://doi.org/10.1145/3388440.3412415).