Vertaile menetelmiä
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| Assosiaatiosääntöjen louhinta (Apriori)× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1994 | 2000 |
| Kehittäjä≠ | Rakesh Agrawal & Ramakrishnan Srikant | Jiawei Han, Jian Pei & Yiwen Yin |
| Tyyppi≠ | Unsupervised pattern discovery algorithm | Frequent-itemset mining algorithm |
| Alkuperäislähde≠ | Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216. DOI ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Rinnakkaisnimet | Market Basket Analysis, Frequent Itemset Mining, Birliktelik Kuralı Madenciliği, Itemset Association Analysis | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Liittyvät≠ | 3 | 4 |
| Tiivistelmä≠ | 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. | FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets. |
| ScholarGateAineisto ↗ |
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