Understanding Complex Datasets: Data Mining with Matrix DecompositionsCRC Press, 17 Mei 2007 - 260 halaman Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book |
Isi
Data Mining | 1 |
Matrix decompositions | 23 |
Singular Value Decomposition SVD | 49 |
Graph Analysis | 91 |
SemiDiscrete Decomposition SDD | 123 |
Using SVD and SDD together | 141 |
Independent Component Analysis ICA | 155 |
NonNegative Matrix Factorization NNMF | 173 |
Tensors | 189 |
Conclusion | 197 |
Matlab Scripts to generate example matrix decompositions | 203 |
| 223 | |
| 233 | |
Edisi yang lain - Lihat semua
Understanding Complex Datasets: Data Mining with Matrix Decompositions David Skillicorn Pratinjau tidak tersedia - 2007 |
Istilah dan frasa umum
3-dimensional plot adjacency matrix affinity algorithm analysis applied attribute values axes biclusters bump ButtonDownFcn captures collaborative filtering Color Figure complex compute connected consider contain correlation matrix corresponding customers data matrix data mining dataset matrix described diagonal dimensions distance dmfn documents dot product edges eigenvalues Eigenvector and graph eigenvectors end folose entries Euclidean example matrix factors following page 138 FontSize fopen Gaussian geometric space graph space hierarchical clustering important Independent Component Analysis insert following int2str kind labelled Laplacian magnitude matrix decompositions NNMF nodes noise non-negative Non-Negative Matrix Factorization normalized num2str(i objects and attributes original orthogonal outer product outliers plot3 plot3(u points position possible prediction problem properties rank region relationships removed represent representation matrix rows of F samples similar singular value decomposition singular values sparse statistically independent structure switch split target attribute tensor decomposition truncated Tucker3 vectors weight words zero
Bagian yang populer
Halaman 223 - References 1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases.
Halaman 223 - Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, August 29-September 1 1994.
Halaman 230 - In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp.
Halaman 229 - Learning the parts of objects by nonnegative matrix factorization,
Halaman 226 - Report on the existence of a global system for the interception of private and commercial communications (ECHELON interception system) (2001/2098(INI)) (July 11, 2001).
Halaman 226 - H. Drucker, CJC Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 155-161, Cambridge, MA, 1997. MIT Press. 4. B. Bhanu, and J Ahn, "A system for model-based recognition of articulated objects," Proceedings, International Conference on Pattern Recognition, pp.
Halaman 223 - References 1. D. Achlioptas and F. McSherry. Fast computation of low rank matrix approximations.