Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Пояснюваний K-Means× | Ієрархічна кластеризація× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2020 | 1963 |
| Автор методу≠ | Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C. | Ward, J. H. |
| Тип≠ | Explainable unsupervised clustering algorithm | Unsupervised clustering (agglomerative) |
| Основоположне джерело≠ | 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 ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Інші назви≠ | ExKMC, interpretable k-means, decision-tree k-means, explainable clustering | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
|
|