Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| K-means Nusu-Simamiwa× | Uainishaji wa K-means× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2001–2002 | 1967 (formalized 1982) |
| Mwanzilishi≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | MacQueen, J. B.; Lloyd, S. P. |
| Aina≠ | Semi-supervised clustering | Partitional clustering |
| Chanzo asilia≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Majina mbadala | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | 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. |
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