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| K-Means klasterovanje× | Негативна матрична факторизација (NMF)× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica≠ | Machine learning | Latent structure |
| Godina nastanka≠ | 1967 | 1999 |
| Tvorac≠ | MacQueen, J. | Lee, D. D. & Seung, H. S. |
| Tip≠ | Partitional clustering (centroid-based) | Matrix decomposition with non-negativity constraints |
| Temeljni izvor≠ | 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. link ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| Drugi nazivi≠ | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering | NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation |
| Srodne≠ | 3 | 4 |
| Sažetak≠ | 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. |
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