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| Förklarbar HDBSCAN× | Förklaringsbar K-medelvärden× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2017–2020 | 2020 |
| Upphovsperson≠ | McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation) | Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C. |
| Typ≠ | Explainable clustering | Explainable unsupervised clustering algorithm |
| Ursprungskälla≠ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ | Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119. link ↗ |
| Alias | XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI | ExKMC, interpretable k-means, decision-tree k-means, explainable clustering |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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 K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable. |
| ScholarGateDatamängd ↗ |
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