Understanding Complex Datasets: Data Mining with Matrix Decompositions

David Skillicorn

May 17, 2007 by Chapman and Hall/CRC
Reference - 258 Pages - 18 Color & 84 B/W Illustrations
ISBN 9781584888321 - CAT# C8326
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


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  • Focuses on singular value decomposition, semidiscrete decomposition, independent component analysis, non-negative matrix factorization, and tensors
  • Matches the proper matrix analysis technique to real-world scientific and engineering systems
  • Discusses several important theoretical and algorithmic problems of matrix decompositions, such as instability
  • Applies matrix decompositions to the diverse fields of information retrieval, topic detection, geochemistry, astrophysics, microarray analysis, process control, counterterrorism, and social network analysis
  • Provides MATLAB® scripts to generate examples of matrix decompositions and URLs where tools can be downloaded
  • Summary

    Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book helps you determine which matrix is appropriate for your dataset and what the results mean.

    Explaining the effectiveness of matrices as data analysis tools, the book illustrates the ability of matrix decompositions to provide more powerful analyses and to produce cleaner data than more mainstream techniques. The author explores the deep connections between matrix decompositions and structures within graphs, relating the PageRank algorithm of Google's search engine to singular value decomposition. He also covers dimensionality reduction, collaborative filtering, clustering, and spectral analysis. With numerous figures and examples, the book shows how matrix decompositions can be used to find documents on the Internet, look for deeply buried mineral deposits without drilling, explore the structure of proteins, detect suspicious emails or cell phone calls, and more.

    Concentrating on data mining mechanics and applications, this resource helps you model large, complex datasets and investigate connections between standard data mining techniques and matrix decompositions.