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| K-Lân Cận Gần Nhất Có Thể Giải Thích× | HDBSCAN× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 1967 (KNN); 2010s (explainability extensions) | 2013 |
| Người khởi xướng≠ | Cover, T. & Hart, P. (KNN); XAI extensions by various authors | Campello, R. J. G. B.; Moulavi, D.; Sander, J. |
| Loại≠ | Instance-based learning with explainability layer | Hierarchical density-based clustering |
| Công trình gốc≠ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg. DOI ↗ |
| Tên gọi khác | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. | HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions. |
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