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| Grupowanie K-średnich (K-means Clustering)× | Uczenie ze wsparciem częściowym× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1967 (formalized 1982) | 1970s–2006 (formalized) |
| Twórca≠ | MacQueen, J. B.; Lloyd, S. P. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Partitional clustering | Learning paradigm |
| Źródło pierwotne≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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