Algoritma Apriori Separuh-Terbimbing
Algoritma Apriori Separuh-Terbimbing (Semi-supervised Apriori) memperluas penambang set-item lazim (frequent-itemset miner) klasik Apriori dengan menyuntikkan pengetahuan latar belakang atau kekangan berlabel — seperti pasangan mesti-serasi (must-link pairs), item terlarang, atau ambang sokongan minimum yang ditentukan pengguna per kumpulan — untuk memihak penemuan ke arah peraturan persatuan (association rules) yang bermakna secara praktikal dan mengurangkan ruang carian.
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Method map
The neighbourhood of related methods — select a node to explore.
Sumber
- 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 ↗
- Liu, B., Hsu, W., & Ma, Y. (1999). Mining association rules with multiple minimum supports. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 337–341. DOI: 10.1145/312129.312274 ↗
Cara memetik halaman ini
ScholarGate. (2026, June 3). Semi-supervised Apriori Algorithm for Constrained Association Rule Mining. ScholarGate. https://scholargate.app/ms/machine-learning/semi-supervised-apriori-algorithm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Galian Peraturan Persatuan (Apriori)Pembelajaran Mesin↔ compare
- Penapisan KolaboratifPembelajaran Mesin↔ compare
- FP-Growth (Pertumbuhan Corak Kerap)Pembelajaran Mesin↔ compare
- Pembelajaran Separa SeliaPembelajaran Mesin↔ compare
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