Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| K-Means shlukování× | Lokálně lineární vnoření (LLE)× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1967 | 2000 |
| Tvůrce≠ | MacQueen, J. | Sam Roweis & Lawrence Saul |
| Typ≠ | Partitional clustering (centroid-based) | Nonlinear manifold dimensionality reduction |
| Původní zdroj≠ | 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 ↗ | Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. DOI ↗ |
| Další názvy | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering | LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme |
| Příbuzné | 3 | 3 |
| Shrnutí≠ | 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. | Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map. |
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