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| Klastrowanie spektralne× | Grupowanie K-średnich (K-means Clustering)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2002 | 1967 (formalized 1982) |
| Twórca≠ | Ng, A. Y.; Jordan, M. I.; Weiss, Y. | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Graph-based clustering (spectral method) | Partitional clustering |
| Źródło pierwotne≠ | Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Inne nazwy≠ | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate. | 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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