Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Stochastic Block Model× | DBSCAN× | Hiërarchische clustering× | |
|---|---|---|---|
| Vakgebied≠ | Netwerkanalyse | Machine learning | Machine learning |
| Familie≠ | Process / pipeline | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1983 | 1996 | 1963 |
| Grondlegger≠ | — | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Ward, J. H. |
| Type≠ | Probabilistic generative graph model | Density-based clustering algorithm | Unsupervised clustering (agglomerative) |
| Oorspronkelijke bron≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. 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 ↗ |
| Aliassen≠ | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Verwant≠ | 7 | 3 | 4 |
| Samenvatting≠ | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network 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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