Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms) - ISBN:0898716268

Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms)

Date: 14 April 2011, 11:54

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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 MATLABA®. 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 GoogleA” search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

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