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	Clustering K-Means	Fattorizzazione di Matrici Non-Negative (NMF)
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Latent structure
Anno di origine	1967	1999
Ideatore	MacQueen, J.	Lee, D. D. & Seung, H. S.
Tipo	Partitional clustering (centroid-based)	Matrix decomposition with non-negativity constraints
Fonte seminale	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.
Alias	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Correlati	3	4
Sintesi	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.