Salīdzināt metodes

	Neatūru matricas faktorizācija (NMF)	K-Means klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Latent structure	Machine learning
Izcelsmes gads	1999	1967
Autors	Lee, D. D. & Seung, H. S.	MacQueen, J.
Tips	Matrix decomposition with non-negativity constraints	Partitional clustering (centroid-based)
Pirmavots	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	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.
Citi nosaukumi	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Saistītās	4	3
Kopsavilkums	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.	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.