Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| K-keskmiste klasterdamine× | DBSCAN× | Hierarchical Clustering× | |
|---|---|---|---|
| Valdkond | Masinõpe | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning | Machine learning |
| Tekkeaasta≠ | 1967 (formalized 1982) | 1996 | 1963 |
| Looja≠ | MacQueen, J. B.; Lloyd, S. P. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Ward, J. H. |
| Tüüp≠ | Partitional clustering | Density-based clustering algorithm | Unsupervised clustering (agglomerative) |
| Algallikas≠ | 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 ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Rööpnimetused≠ | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Seotud≠ | 4 | 3 | 4 |
| Kokkuvõte≠ | 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. | 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. |
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