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	Klasteryzacja K-średnich	NMF (Non-negative Matrix Factorization)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Latent structure
Rok powstania	1967	1999
Twórca	MacQueen, J.	Lee, D. D. & Seung, H. S.
Typ	Partitional clustering (centroid-based)	Matrix decomposition with non-negativity constraints
Źródło pierwotne	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.
Inne nazwy	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Pokrewne	3	4
Podsumowanie	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.