Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Explainable K-Means× | Beslisboom× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2020 | 1984 |
| Grondlegger≠ | Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C. | Breiman, Friedman, Olshen & Stone |
| Type≠ | Explainable unsupervised clustering algorithm | Recursive partitioning (if-then rules) |
| 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 ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliassen≠ | ExKMC, interpretable k-means, decision-tree k-means, explainable clustering | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Verwant | 5 | 5 |
| 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
| ScholarGateGegevensset ↗ |
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