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| 설명 가능한 HDBSCAN× | 설명 가능한 DBSCAN× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2020 | 1996 (DBSCAN); 2010s (XAI integration) |
| 창시자≠ | McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation) | Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP) |
| 유형≠ | Explainable clustering | Unsupervised clustering with post-hoc interpretability |
| 원전≠ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ | Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press. link ↗ |
| 별칭 | XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI | XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation. |
| ScholarGate데이터셋 ↗ |
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