Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Видобування асоціативних правил (Apriori)× | Дерево рішень× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1994 | 1984 |
| Автор методу≠ | Rakesh Agrawal & Ramakrishnan Srikant | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Unsupervised pattern discovery algorithm | Recursive partitioning (if-then rules) |
| Основоположне джерело≠ | Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Інші назви≠ | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Пов'язані≠ | 3 | 5 |
| Підсумок≠ | Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift. | 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. |
| ScholarGateНабір даних ↗ |
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