Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| FP-Growth (Frequent Pattern Growth)× | Uainishaji wa K-means× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2000 | 1967 (formalized 1982) |
| Mwanzilishi≠ | Jiawei Han, Jian Pei & Yiwen Yin | MacQueen, J. B.; Lloyd, S. P. |
| Aina≠ | Frequent-itemset mining algorithm | Partitional clustering |
| Chanzo asilia≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Majina mbadala | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
| ScholarGateSeti ya data ↗ |
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