×

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.

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

Software:

GraphDemo; Matlab
PDFBibTeX XMLCite
Full Text: DOI