方法对比
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| 可解释K-Means× | DBSCAN× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 1996 |
| 提出者≠ | Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| 类型≠ | Explainable unsupervised clustering algorithm | Density-based clustering algorithm |
| 开创性文献≠ | 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 ↗ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ |
| 别名≠ | ExKMC, interpretable k-means, decision-tree k-means, explainable clustering | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. |
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