So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phân cụm K-means× | Học trực tuyến× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 1967 (formalized 1982) | 1958–2000s |
| Người khởi xướng≠ | MacQueen, J. B.; Lloyd, S. P. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Loại≠ | Partitional clustering | Learning paradigm (sequential model update) |
| Công trình gốc≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Tên gọi khác | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | incremental learning, sequential learning, streaming learning, online machine learning |
| Liên quan≠ | 4 | 6 |
| Tóm tắt≠ | 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
| ScholarGateBộ dữ liệu ↗ |
|
|