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| Phân cụm phân cấp× | Phân cụm K-means× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 1963 | 1967 (formalized 1982) |
| Người khởi xướng≠ | Ward, J. H. | MacQueen, J. B.; Lloyd, S. P. |
| Loại≠ | Unsupervised clustering (agglomerative) | Partitional clustering |
| Công trình gốc≠ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Tên gọi khác≠ | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Liên quan | 4 | 4 |
| Tóm tắt≠ | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. | 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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