Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Assosiasjonsregelutvinning (Apriori)× | Beslutningstre× | |
|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 1994 | 1984 |
| Opphavsperson≠ | Rakesh Agrawal & Ramakrishnan Srikant | Breiman, Friedman, Olshen & Stone |
| Type≠ | Unsupervised pattern discovery algorithm | Recursive partitioning (if-then rules) |
| Opprinnelig kilde≠ | 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 ↗ |
| Alias≠ | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Relaterte≠ | 3 | 5 |
| Sammendrag≠ | 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. |
| ScholarGateDatasett ↗ |
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