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| Robuster k-Means× | K-means Clustering× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1999 | 1967 (formalized 1982) |
| Urheber≠ | Garcia-Escudero, L. A. & Gordaliza, A. | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Robust clustering algorithm | Partitional clustering |
| Wegweisende Quelle≠ | Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Aliasnamen | robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down. | 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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