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| 준지도식 Apriori 알고리즘× | 준지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1999–2005 | 1970s–2006 (formalized) |
| 창시자≠ | Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 유형≠ | Constrained association rule mining algorithm | Learning paradigm |
| 원전≠ | Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 별칭 | constrained Apriori, semi-supervised ARM, knowledge-guided Apriori, labeled-constraint Apriori | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 관련≠ | 4 | 5 |
| 요약≠ | The Semi-supervised Apriori algorithm extends the classic Apriori frequent-itemset miner by injecting background knowledge or labeled constraints — such as must-link pairs, forbidden items, or user-specified minimum support thresholds per group — to bias discovery toward practically meaningful association rules and reduce the search space. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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