השוואת שיטות

	אשכול K-means	למידה בפיקוח עצמי
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1967 (formalized 1982)	2018–2020
הוגה השיטה	MacQueen, J. B.; Lloyd, S. P.	LeCun, Y. and community (formalized ~2018–2020)
סוג	Partitional clustering	Representation learning paradigm
מקור מכונן	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	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/
כינויים	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
קשורות	4	3
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.	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.