Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Explainable K-Means× | DBSCAN× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2020 | 1996 |
| Grondlegger≠ | Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Type≠ | Explainable unsupervised clustering algorithm | Density-based clustering algorithm |
| Oorspronkelijke bron≠ | Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119. link ↗ | 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 ↗ |
| Aliassen≠ | ExKMC, interpretable k-means, decision-tree k-means, explainable clustering | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable. | 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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