השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| DBSCAN× | אשכול K-means× | למידה בפיקוח עצמי× | |
|---|---|---|---|
| תחום | למידת מכונה | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 1996 | 1967 (formalized 1982) | 2018–2020 |
| הוגה השיטה≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | MacQueen, J. B.; Lloyd, S. P. | LeCun, Y. and community (formalized ~2018–2020) |
| סוג≠ | Density-based clustering algorithm | Partitional clustering | Representation learning paradigm |
| מקור מכונן≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| כינויים≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| קשורות≠ | 3 | 4 | 3 |
| תקציר≠ | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | 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. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGateמערך נתונים ↗ |
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