Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Самокерований K-середніх× | Кластеризація методом k-середніх× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2018 | 1967 (formalized 1982) |
| Автор методу≠ | Caron, M. et al. (DeepCluster framework) | MacQueen, J. B.; Lloyd, S. P. |
| Тип≠ | Self-supervised clustering | Partitional clustering |
| Основоположне джерело≠ | Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Інші назви | self-supervised clustering with K-means, deep clustering with K-means, unsupervised K-means with pseudo-labels, SSL K-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth. | 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. |
| ScholarGateНабір даних ↗ |
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