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| Grupowanie K-średnich (K-means Clustering)× | Półnadzorowany HDBSCAN× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1967 (formalized 1982) | 2017–present |
| Twórca≠ | MacQueen, J. B.; Lloyd, S. P. | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors |
| Typ≠ | Partitional clustering | Semi-supervised density-based clustering |
| Źródło pierwotne≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ |
| Inne nazwy | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN |
| Pokrewne≠ | 4 | 6 |
| 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 HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge. |
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