השוואת שיטות
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| אשכול K-means× | למידה מונחית-למחצה× | אנסמבל הצבעה× | |
|---|---|---|---|
| תחום | למידת מכונה | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning | Machine learning |
| שנת המקור≠ | 1967 (formalized 1982) | 1970s–2006 (formalized) | 1990s–2004 |
| הוגה השיטה≠ | MacQueen, J. B.; Lloyd, S. P. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| סוג≠ | Partitional clustering | Learning paradigm | Ensemble (combination of multiple classifiers by vote) |
| מקור מכונן≠ | 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 | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| כינויים | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| קשורות≠ | 4 | 5 | 5 |
| תקציר≠ | 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. | A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted. |
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