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| Kauzális feltáró algoritmusok (PC, FCI, LiNGAM)× | DBSCAN× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Gépi tanulás |
| Módszercsalád≠ | Regression model | Machine learning |
| Keletkezés éve≠ | 2000 | 1996 |
| Megalkotó≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Típus≠ | Causal structure learning | Density-based clustering algorithm |
| Alapmű≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | 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 ↗ |
| Alternatív nevek≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | 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. |
| ScholarGateAdatkészlet ↗ |
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