# Generating Multidimensional Clusters With Support Lines

arXiv: https://arxiv.org/abs/2301.10327 · HTML: https://mlsanity.com/paper/2301.10327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 67 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/1810.05691/) (2018)
- [Evaluation Metrics for Unsupervised Learning Algorithms](https://mlsanity.com/paper/1905.05667/) (2019)
- [Plots.jl -- a user extendable plotting API for the julia programming language](https://mlsanity.com/paper/2204.08775/) (2022)
- [HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis](https://mlsanity.com/paper/2102.06940/) (2021)

## Built on by (most cited first, 1 of 1)

- [GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries](https://mlsanity.com/paper/2508.00033/) (2025)

## Abstract (arXiv)

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.