Understanding Complex Datasets

Focusing on data mining mechanics and applications, this book explores the most common matrix decompositions, including singular value, semidiscrete, independent component analysis, non-negative matrix factorization, and tensors. It shows how these matrix decompositions can be used to analyze large datasets in a broad range of application areas, such as information retrieval, topic detection, geochemistry, astrophysics, microarray analysis, process control, counterterrorism, and social network analysis. The book also discusses several important theoretical and algorithmic problems of matrix decompositions and provides MATLAB® scripts to generate examples of matrix decompositions.