Linear Algebra Tools for Data Mining

  • 11h 33m
  • Dan A. Simovici
  • World Scientific Publishing Co
  • 2012

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.

In this Book

  • Modules and Linear Spaces
  • Matrices
  • 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
  • Spectral Properties of Graphs and Spectral Clustering
  • Bibliography