Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Arbore de decizie× | Clustering K-Means× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1984 | 1967 |
| Autorul original≠ | Breiman, Friedman, Olshen & Stone | MacQueen, J. |
| Tip≠ | Recursive partitioning (if-then rules) | Partitional clustering (centroid-based) |
| Sursa seminală≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ |
| Denumiri alternative≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. |
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