Supervised Clustered Interpretability: Explainable Subgroup Discovery via Cluster Separation and Partial Dependence Disparity

Published Online:https://doi.org/10.1287/ijoc.2024.0657

References

  • Agarwal R, Melnick L, Frosst N, Zhang X, Lengerich B, Caruana R, Hinton GE (2021) Neural additive models: Interpretable machine learning with neural nets. Adv. Neural Inform. Processing Systems 34:4699–4711.Google Scholar
  • Baucum M, Rabiee M (2026) Supervised clustered interpretability: Explainable subgroup discovery via cluster separation and partial dependence disparity. https://doi.org/10.1287/ijoc.2024.0657.cd, https://github.com/INFORMSJoC/2024.0657.Google Scholar
  • Bertsimas D, Orfanoudaki A, Wiberg H (2021) Interpretable clustering: An optimization approach. Machine Learn. 110:89–138.CrossrefGoogle Scholar
  • Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Comm. Statist. 3(1):1–27.CrossrefGoogle Scholar
  • Carrizosa E, Kurishchenko K, Marín A, Morales DR (2023) On clustering and interpreting with rules by means of mathematical optimization. Comput. Oper. Res. 154:106180.CrossrefGoogle Scholar
  • Clement T, Nguyen HTT, Kemmerzell N, Abdelaal M, Stjelja D (2024) Beyond explaining: XAI-based adaptive learning with SHAP clustering for energy consumption prediction. Preprint, submitted February 7, https://arxiv.org/abs/2402.04982.Google Scholar
  • Cooper A, Doyle O, Bourke A (2021) Supervised clustering for subgroup discovery: An application to COVID-19 symptomatology. Kamp M, et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021, Communications in Computer and Information Science, vol. 1525 (Springer, Cham, Switzerland), 408–422.Google Scholar
  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intelligence PAMI-1(2):224–227.CrossrefGoogle Scholar
  • Fraiman R, Ghattas B, Svarc M (2013) Interpretable clustering using unsupervised binary trees. Adv. Data Anal. Classification 7:125–145.CrossrefGoogle Scholar
  • Gosiewska A, Biecek P (2019) Do not trust additive explanations. Preprint, submitted March 27, https://arxiv.org/abs/1903.11420.Google Scholar
  • Handl J, Knowles J (2005) Cluster generators for large high-dimensional data sets with large numbers of clusters. Dimension 2:20, https://personalpages.manchester.ac.uk/staff/Julia.Handl/generators.pdf.Google Scholar
  • Hjort A, Scheel I, Sommervoll DE, Pensar J (2024) Locally interpretable tree boosting: An application to house price prediction. Decision Support Systems 178:114106.CrossrefGoogle Scholar
  • Hu L, Jiang M, Dong J, Liu X, He Z (2026) Interpretable clustering: A survey. ACM Comput. Surveys 58(8):1–21.Google Scholar
  • Ikotun AM, Ezugwu AE, Abualigah L, Abuhaija B, Heming J (2023) K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inform. Sci. 622:178–210.CrossrefGoogle Scholar
  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv. Neural Inform. Processing Systems 30.Google Scholar
  • Moshkovitz M, Dasgupta S, Rashtchian C, Frost N (2020) Explainable k-means and k-medians clustering. Internat. Conf. Machine Learn. (PMLR), 7055–7065.Google Scholar
  • Nori H, Jenkins S, Koch P, Caruana R (2019) InterpretML: A unified framework for machine learning interpretability. Preprint, submitted September 19, https://arxiv.org/abs/1909.09223.Google Scholar
  • Peng X, Li Y, Tsang IW, Zhu H, Lv J, Zhou JT (2022) XAI beyond classification: Interpretable neural clustering. J. Machine Learn. Res. 23(6):1–28.Google Scholar
  • Ren Y, Pu J, Yang Z, Xu J, Li G, Pu X, Yu PS, He L (2025) Deep clustering: A comprehensive survey. IEEE Trans. Neural Network Learn. Systems 36(4):5858–5878.CrossrefGoogle Scholar
  • Ribeiro MT, Singh S, Guestrin C (2016) Model-agnostic interpretability of machine learning. Preprint, submitted June 16, https://arxiv.org/abs/1606.05386.Google Scholar
  • Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20:53–65.CrossrefGoogle Scholar
  • Saisubramanian S, Galhotra S, Zilberstein S (2020) Balancing the tradeoff between clustering value and interpretability. Proc. AAAI/ACM Conf. AI Ethics Soc. (Association for Computing Machinery, New York), 351–357.Google Scholar
  • Sevilla-Villanueva B, Gibert K, Sànchez-Marrè M (2017) A methodology to discover and understand complex patterns: Interpreted integrative multiview clustering. Pattern Recognition Lett. 93:85–94.CrossrefGoogle Scholar
  • Zheng L, Zhu Y, He J (2023) Fairness-aware multi-view clustering. Proc. 2023 SIAM Internat. Conf. Data Mining (SDM) (SIAM, Philadelphia), 856–864.Google Scholar
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