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| DBSCAN× | Phân cụm phân cấp× | Máy Vectơ Hỗ trợ (Phân loại)× | |
|---|---|---|---|
| Lĩnh vực | Học máy | Học máy | Học máy |
| Họ | Machine learning | Machine learning | Machine learning |
| Năm ra đời≠ | 1996 | 1963 | 1995 |
| Người khởi xướng≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Ward, J. H. | Cortes, C. & Vapnik, V. |
| Loại≠ | Density-based clustering algorithm | Unsupervised clustering (agglomerative) | Maximum-margin classifier (kernel method) |
| Công trình gốc≠ | 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 ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Tên gọi khác≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Liên quan≠ | 3 | 4 | 5 |
| Tóm tắt≠ | 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. | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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