विधियों की तुलना करें

	व्याख्यायोग्य गॉसियन मिश्रण मॉडल	के-मीन्स क्लस्टरिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1995–2020s	1967
प्रवर्तक	Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors	MacQueen, J.
प्रकार	Probabilistic clustering with post-hoc or built-in explainability	Partitional clustering (centroid-based)
मौलिक स्रोत	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
उपनाम	X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
संबंधित	3	3
सारांश	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.