{"ID":2847001,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00885","arxiv_id":"2511.00885","title":"SpEx: A Spectral Approach to Explainable Clustering","abstract":"Explainable clustering by axis-aligned decision trees was introduced by Moshkovitz et al. (2020) and has gained considerable interest. Prior work has focused on minimizing the price of explainability for specific clustering objectives, lacking a general method to fit an explanation tree to any given clustering, without restrictions. In this work, we propose a new and generic approach to explainable clustering, based on spectral graph partitioning. With it, we design an explainable clustering algorithm that can fit an explanation tree to any given non-explainable clustering, or directly to the dataset itself. Moreover, we show that prior algorithms can also be interpreted as graph partitioning, through a generalized framework due to Trevisan (2013) wherein cuts are optimized in two graphs simultaneously. Our experiments show the favorable performance of our method compared to baselines on a range of datasets.","short_abstract":"Explainable clustering by axis-aligned decision trees was introduced by Moshkovitz et al. (2020) and has gained considerable interest. Prior work has focused on minimizing the price of explainability for specific clustering objectives, lacking a general method to fit an explanation tree to any given clustering, without...","url_abs":"https://arxiv.org/abs/2511.00885","url_pdf":"https://arxiv.org/pdf/2511.00885v1","authors":"[\"Tal Argov\",\"Tal Wagner\"]","published":"2025-11-02T10:49:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DS\"]","methods":"[]","has_code":false}