השוואת שיטות

	אשכול K-Means	פירוק מטריצות לא-שליליות (NMF)	Word2Vec
תחום	למידת מכונה	למידת מכונה	כריית טקסט
משפחה	Machine learning	Latent structure	Process / pipeline
שנת המקור	1967	1999	2013
הוגה השיטה	MacQueen, J.	Lee, D. D. & Seung, H. S.	Tomas Mikolov et al.
סוג	Partitional clustering (centroid-based)	Matrix decomposition with non-negativity constraints	Neural word-embedding model
מקור מכונן	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
כינויים	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
קשורות	3	4	4
תקציר	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.