Numerical linear algebra in data mining
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, lowrank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.
 Publication:

Acta Numerica
 Pub Date:
 2006
 DOI:
 10.1017/S0962492906240017
 Bibcode:
 2006AcNum..15..327E