Simovici, Dan A. Linear algebra tools for data mining. (English) Zbl 1246.15001 Hackensack, NJ: World Scientific (ISBN 978-981-4383-49-3/hbk; 978-981-4383-50-9/ebook). xiv, 863 p. (2012). The book is divided in two parts and is intended to graduate students and researchers who have concerns in data mining and pattern recognition. In order to help the readers interested in applications presented in this volume, the author includes in the first part the most of the mathematical background that is needed: modules and linear spaces, matrices, interactive system – MATLAB, determinants, norms, inner product, convexity, eigenvalues, similarity and spectra, singular values. In the second part “Applications” included are: graphs, sample matrices, biplots, least squares approximation, principal component analysis, the \(k\)-means algorithm and convexity, spectral clustering algorithms, etc. Reviewer: Costică Moroşanu (Iaşi) Cited in 1 ReviewCited in 1 Document MSC: 15-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to linear algebra 65Fxx Numerical linear algebra 68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science 68T05 Learning and adaptive systems in artificial intelligence 68Q32 Computational learning theory 62H25 Factor analysis and principal components; correspondence analysis 68T10 Pattern recognition, speech recognition 62H30 Classification and discrimination; cluster analysis (statistical aspects) 15A15 Determinants, permanents, traces, other special matrix functions 15A60 Norms of matrices, numerical range, applications of functional analysis to matrix theory 15A18 Eigenvalues, singular values, and eigenvectors Keywords:linear algebra; matrix theory; numerical linear algebra; computer science; learning theory; textbook; data mining; pattern recognition; modules; interactive system; MATLAB; determinants; norms; inner product; eigenvalues; similarity; spectra; singular values; graphs; sample matrices; biplots; least squares approximation; principle component analysis; \(k\)-means algorithm; convexity; spectral clustering Software:GraphDemo; Matlab PDFBibTeX XMLCite \textit{D. A. Simovici}, Linear algebra tools for data mining. Hackensack, NJ: World Scientific (2012; Zbl 1246.15001) Full Text: DOI