Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Geregulariseerde K-Means Clustering× | K-means Clustering× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2010 | 1967 (formalized 1982) |
| Grondlegger≠ | Witten, D. M. & Tibshirani, R. (sparse k-means formulation) | MacQueen, J. B.; Lloyd, S. P. |
| Type≠ | Regularized unsupervised clustering | Partitional clustering |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | sparse k-means, penalized k-means, regularized clustering, constrained k-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Verwant≠ | 2 | 4 |
| Samenvatting≠ | 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. |
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