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| Model Campuran Gaussian Boleh Jelas× | Pengelompokan K-Means× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 1995–2020s | 1967 |
| Pengasas≠ | Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors | MacQueen, J. |
| Jenis≠ | Probabilistic clustering with post-hoc or built-in explainability | Partitional clustering (centroid-based) |
| Sumber perintis≠ | 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. link ↗ |
| Alias | X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Berkaitan | 3 | 3 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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