Linear Algebra Tools for Data Mining

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. Contents:Linear Algebra:Modules and Linear SpacesMatricesMATLABDeterminantsNorms on Linear SpacesInner Product SpacesConvexityEigenvaluesSimilarity and SpectraSingular ValuesApplications:Graphs and MatricesData Sample MatricesLeast Squares Approximation and Data MiningDimensionality Reduction TechniquesThe k-Means ClusteringSpectral Properties of Graphs and Spectral Clustering Readership: Professionals, academics, and graduate students in pattern recognition and artificial intelligence. Keywords:Data Mining;Linear Algebra;Machine Learning;Pattern RecognitionKey Features:Integrates the mathematical developments to their applications in data mining without sacrificing the mathematical rigorPresented applications with full mathematical justifications and are often accompanied by MATLAB codeHighlights strong links between linear algebra, topology and graph theory because these links are essentially important for applicationsA self-contained book that deals with mathematics that is immediately relevant for data mining