Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Поиск ассоциативных правил (Apriori)× | Дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1994 | 1984 |
| Автор метода≠ | Rakesh Agrawal & Ramakrishnan Srikant | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Unsupervised pattern discovery algorithm | Recursive partitioning (if-then rules) |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Связанные≠ | 3 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
|
|