So sánh phương pháp

	Mô hình Hỗn hợp Gaussian Giải thích được	Phân tích Lớp Ẩn (LCA)
Lĩnh vực	Học máy	Thống kê
Họ	Machine learning	Latent structure
Năm ra đời	1995–2020s	1950s–1968
Người khởi xướng	Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors	Paul F. Lazarsfeld
Loại	Probabilistic clustering with post-hoc or built-in explainability	Latent variable / person-centered classification
Công trình gốc	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Tên gọi khác	X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Liên quan	3	6
Tóm tắt	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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.