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Numerical linear algebra in data mining

Published online by Cambridge University Press:  16 May 2006

Lars Eldén
Affiliation:
Department of Mathematics, Linköping University, SE-581 83 Linköping, Sweden E-mail: [email protected]

Abstract

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

Type
Research Article
Copyright
2006 Cambridge University Press

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