方法对比
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| K-Means聚类× | 层次聚类× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1967 | 1963 |
| 提出者≠ | MacQueen, J. | Ward, J. H. |
| 类型≠ | Partitional clustering (centroid-based) | Unsupervised clustering (agglomerative) |
| 开创性文献≠ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| 别名≠ | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| 相关≠ | 3 | 4 |
| 摘要≠ | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. | 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. |
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