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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Clustering gerarchico× | DBSCAN× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1963 | 1996 |
| Ideatore≠ | Ward, J. H. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Tipo≠ | Unsupervised clustering (agglomerative) | Density-based clustering algorithm |
| Fonte seminale≠ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ | 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 ↗ |
| Alias≠ | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | 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. | 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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