قارن الطرق
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| النمو شبه المُشرف عليه للأنماط المتكررة (Semi-supervised FP-growth)× | شجرة القرار (Decision Tree)× | |
|---|---|---|
| المجال | تعلم الآلة | تعلم الآلة |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2000s–2010s | 1984 |
| صاحب الطريقة≠ | Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s | Breiman, Friedman, Olshen & Stone |
| النوع≠ | Semi-supervised frequent pattern mining | Recursive partitioning (if-then rules) |
| المصدر التأسيسي≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 1–12. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| الأسماء البديلة≠ | SS-FP-growth, constrained FP-growth, label-guided frequent pattern mining, semi-supervised frequent itemset mining | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| ذات صلة≠ | 3 | 5 |
| الملخص≠ | Semi-supervised FP-growth extends the classical Frequent Pattern growth algorithm by incorporating partial labels, user-defined constraints, or class-level information to guide frequent itemset discovery. Instead of mining all patterns indiscriminately, it focuses on patterns that are both statistically frequent and semantically meaningful given the available supervision signal. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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