Description

Book Synopsis
This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and pattern recognition, as shown by the many current data mining publications, and has a strong impact in other disciplines like psychology, chemistry, and biology. The basic material is accompanied by more than 550 exercises and supplements, many accompanied with complete solutions and MATLAB applications.

Table of Contents
Modules and Linear Spaces; Matrices; MATLAV; Determinants; Norms on Linear Spaces; Inner Product Spaces; Convexity; Eigenvalues; Similarity and Spectra; Singular Values; Graphs and Matrices; Data Sample Matrices; Least Squares Approximation and Data Mining; Dimensionality Reduction Techniques; The K-Means Clustering Algorithm; Spectral Properties of Graphs and Spectral Clustering.

Linear Algebra Tools For Data Mining

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    £252.00

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    RRP £280.00 – you save £28.00 (10%)

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    A Hardback by Dan A Simovici

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      Publisher: World Scientific Publishing Co Pte Ltd
      Publication Date: Publication Date: 01/02/2012
      ISBN13: 9789814383493, 978-9814383493
      ISBN10: 981438349X

      Description

      Book Synopsis
      This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and pattern recognition, as shown by the many current data mining publications, and has a strong impact in other disciplines like psychology, chemistry, and biology. The basic material is accompanied by more than 550 exercises and supplements, many accompanied with complete solutions and MATLAB applications.

      Table of Contents
      Modules and Linear Spaces; Matrices; MATLAV; Determinants; Norms on Linear Spaces; Inner Product Spaces; Convexity; Eigenvalues; Similarity and Spectra; Singular Values; Graphs and Matrices; Data Sample Matrices; Least Squares Approximation and Data Mining; Dimensionality Reduction Techniques; The K-Means Clustering Algorithm; Spectral Properties of Graphs and Spectral Clustering.

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