Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010134190 | QA76.9.D343 E42 2007 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.
Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB#65533;. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms.
The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google? search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.
Table of Contents
Preface |
Part I Linear Algebra Concepts and Matrix Decompositions |
1 Vectors and matrices in data mining and pattern recognition |
2 Vectors and matrices |
3 Linear systems and least squares |
4 Orthogonality |
5 QR decomposition |
6 Singular value decomposition |
7 Reduced rank least squares models |
8 Tensor decomposition |
9 Clustering and non-negative matrix factorization |
Part II Data Mining Applications |
10 Classification of handwritten digits |
11 Text mining |
12 Page ranking for a Web search engine |
13 Automatic key word and key sentence extraction |
14 Face recognition using rensor SVD |
Part III Computing the Matrix Decompositions |
15 Computing Eigenvalues and singular values |
Bibliography |
Index |