方法对比
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| DBSCAN× | 半监督DBSCAN× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1996 | 2000s |
| 提出者≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s) |
| 类型≠ | Density-based clustering algorithm | Constrained density-based clustering |
| 开创性文献≠ | 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 ↗ | 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 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press. link ↗ |
| 别名≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN |
| 相关≠ | 3 | 5 |
| 摘要≠ | 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. | Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points. |
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