Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DBSCAN× | K-means klasterizācija× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1996 | 1967 (formalized 1982) |
| Autors≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | MacQueen, J. B.; Lloyd, S. P. |
| Tips≠ | Density-based clustering algorithm | Partitional clustering |
| Pirmavots≠ | 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 ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Citi nosaukumi≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | 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. |
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