Understanding Complex Datasets through Matrix Decomposition

David Skillcorn's book on matrix decomposition techniques is superb. I especially enjoyed his coverage on non-negative matrix factorization (NNMF) techniques and eigendecomposition (i.e. spectral techniques). I would recommend the book to those interested in data mining and knowledge extraction. The techniques cover a wide range of media and are not simply restricted to relational datasets and textual documents. The treatment of PageRank is concise and articulate: demonstrating the deep relationship between graph mining and learning techniques and matrix decomposition(SVD amongst others) techniques that make search engines such as Google and Bing possible. As a reviewer summarized, "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."

The link provides the complete text of Skillcorn's book in the form of a PDF.