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| Grupowanie K-średnich (K-means Clustering)× | Autoenkoder wariacyjny× | |
|---|---|---|
| Dziedzina≠ | Uczenie maszynowe | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1967 (formalized 1982) | 2014 |
| Twórca≠ | MacQueen, J. B.; Lloyd, S. P. | Kingma, D. P. & Welling, M. |
| Typ≠ | Partitional clustering | Deep generative latent-variable model (encoder–decoder) |
| Źródło pierwotne≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Inne nazwy | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
| ScholarGateZbiór danych ↗ |
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