विधियों की तुलना करें

	व्याख्या योग्य HDBSCAN	व्याख्यायोग्य गॉसियन मिश्रण मॉडल
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017–2020	1995–2020s
प्रवर्तक	McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)	Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors
प्रकार	Explainable clustering	Probabilistic clustering with post-hoc or built-in explainability
मौलिक स्रोत	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9
उपनाम	XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI	X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model
संबंधित	6	3
सारांश	Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, auditable explanation layer.	An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts.