방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 준지도 학습 K-평균 (Semi-supervised K-means)× | K-means 군집화× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001–2002 | 1967 (formalized 1982) |
| 창시자≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | MacQueen, J. B.; Lloyd, S. P. |
| 유형≠ | Semi-supervised clustering | Partitional clustering |
| 원전≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| 별칭 | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| 관련≠ | 5 | 4 |
| 요약≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | 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. |
| ScholarGate데이터셋 ↗ |
|
|