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| Górnictwo sekwencyjnych wzorców (Sequential Pattern Mining)× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1995 | 2000 |
| Twórca≠ | Rakesh Agrawal & Ramakrishnan Srikant | Jiawei Han, Jian Pei & Yiwen Yin |
| Typ≠ | Unsupervised pattern discovery | Frequent-itemset mining algorithm |
| Źródło pierwotne≠ | Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14. DOI ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Inne nazwy | Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis. | 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. |
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