Natural Language-Based Synthetic Data Generation for Cluster Analysis

Michael J. Zellinger () and Peter Bühlmann ()
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Michael J. Zellinger: Department of Computing & Mathematical Sciences, California Institute of Technology
Peter Bühlmann: Seminar for Statistics, ETH Zürich

Journal of Classification, 2025, vol. 42, issue 3, No 3, 517-543

Abstract: Abstract 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 https://repliclust.org 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 .

Keywords: Synthetic data; Simulation; Validation; Clustering; Unsupervised learning; Artificial intelligence; Natural language processing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-025-09501-w

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