Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| DBSCAN× | Метод головних компонент× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1996 | 2002 |
| Автор методу≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Тип≠ | Density-based clustering algorithm | Unsupervised dimensionality reduction |
| Основоположне джерело≠ | 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 ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Інші назви≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Пов'язані | 3 | 3 |
| Підсумок≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateНабір даних ↗ |
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