Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Clustering K-means× | DBSCAN× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1967 (formalized 1982) | 1996 |
| Ideatore≠ | MacQueen, J. B.; Lloyd, S. P. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Tipo≠ | Partitional clustering | Density-based clustering algorithm |
| Fonte seminale≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. 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≠ | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. | 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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