方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 正则化 K-均值聚类× | K-means聚类× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 1967 (formalized 1982) |
| 提出者≠ | Witten, D. M. & Tibshirani, R. (sparse k-means formulation) | MacQueen, J. B.; Lloyd, S. P. |
| 类型≠ | Regularized unsupervised clustering | Partitional clustering |
| 开创性文献≠ | Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| 别名 | sparse k-means, penalized k-means, regularized clustering, constrained k-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| 相关≠ | 2 | 4 |
| 摘要≠ | Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant. | 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. |
| ScholarGate数据集 ↗ |
|
|