Natural Language-Based Synthetic Data Generation for Cluster Analysis
This work addresses the problem of cumbersome benchmark creation for practitioners in cluster analysis, offering a more convenient tool, though it is incremental as it builds on existing synthetic data generation methods.
The paper tackles the laborious process of creating synthetic data for cluster analysis benchmarks by introducing a method that allows direct specification of high-level scenarios through verbal descriptions or parameters, resulting in an open-source Python package called repliclust that facilitates interpretable and reproducible benchmarks.
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of the data sets, such as the overlap between clusters, or the variation in cluster shapes, can be effectively varied. Unfortunately, creating evaluation scenarios is often laborious, as practitioners must translate higher-level scenario descriptions like "clusters with very different shapes" into lower-level geometric parameters such as cluster centers, covariance matrices, etc. To make benchmarks more convenient and informative, we propose synthetic data generation based on direct specification of high-level scenarios, either through verbal descriptions or high-level geometric parameters. Our open-source Python package repliclust implements this workflow, making it easy to set up interpretable and reproducible benchmarks for cluster analysis. A demo of data generation from verbal inputs is available at https://demo.repliclust.org.