Generating Multidimensional Clusters With Support Lines
Abstract
Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem's space. In turn, synthetic data generators have the potential of creating vast amounts of data -- a crucial activity when real-world data is at premium -- while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks.
Cite
@article{arxiv.2301.10327,
title = {Generating Multidimensional Clusters With Support Lines},
author = {Nuno Fachada and Diogo de Andrade},
journal= {arXiv preprint arXiv:2301.10327},
year = {2024}
}
Comments
The peer-reviewed version of this paper is published in Knowledge-Based Systems at https://doi.org/10.1016/j.knosys.2023.110836. This version is typeset by the author and differs only in pagination and typographical detail