Queen's University, Kingston, Ontario, Canada

… One of this book’s attractive features is that every chapter contains a discussion relating to the algorithmic issues. One scenario is used as a running illustrative example throughout the book. Several other examples are discussed in different chapters. These examples should help the reader understand the advantages as well as the practical problems associated with any of the proposed matrix-based data mining techniques covered in the book. I recommend this book for anyone interested in using matrix methods for data mining.—Technometrics, February 2009, Vol. 51, No. 1This could be a nice companion book for courses in data mining or applied linear algebra. Producing a clear taxonomy of the use and intentions of matrix decompositions in data analysis is very useful to both students and researchers. … Those working with large-scale complex datasets will definitely find this work useful. … I would definitely use it in my own course in data mining.—Michael W. Berry, University of Tennessee, Knoxville, USA[This book] is suffused with insightful suggestions for analytical methods and interpretations, drawn from the author's own research and his reading of the literature. …The book has two great strengths. The first is its attempt to provide a unifying framework from which to view a host of important analytical methodologies based on matrix methods. … Second, the book is extremely strong on interpreting the results of matrix methods. … [It] assembles and explains a diverse set of insights that are otherwise widely scattered in the literature. This alone makes the book an important contribution to the community.—Bruce Hendrickson, Sandia National Laboratories, Albuquerque, New Mexico, USA

• Data Mining. Matrix Decompositions. Singular Value Decomposition (SVD). Graph Analysis. SemiDiscrete Decomposition (SDD). Using SVD and SDD Together. Independent Component Analysis (ICA). Non-Negative Matrix Factorization (NNMF). Tensors. Conclusion. Appendix. Bibliography. Index.

Queen's University, Kingston, Ontario, Canada

… One of this book’s attractive features is that every chapter contains a discussion relating to the algorithmic issues. One scenario is used as a running illustrative example throughout the book. Several other examples are discussed in different chapters. These examples should help the reader understand the advantages as well as the practical problems associated with any of the proposed matrix-based data mining techniques covered in the book. I recommend this book for anyone interested in using matrix methods for data mining.—Technometrics, February 2009, Vol. 51, No. 1This could be a nice companion book for courses in data mining or applied linear algebra. Producing a clear taxonomy of the use and intentions of matrix decompositions in data analysis is very useful to both students and researchers. … Those working with large-scale complex datasets will definitely find this work useful. … I would definitely use it in my own course in data mining.—Michael W. Berry, University of Tennessee, Knoxville, USA[This book] is suffused with insightful suggestions for analytical methods and interpretations, drawn from the author's own research and his reading of the literature. …The book has two great strengths. The first is its attempt to provide a unifying framework from which to view a host of important analytical methodologies based on matrix methods. … Second, the book is extremely strong on interpreting the results of matrix methods. … [It] assembles and explains a diverse set of insights that are otherwise widely scattered in the literature. This alone makes the book an important contribution to the community.—Bruce Hendrickson, Sandia National Laboratories, Albuquerque, New Mexico, USA

• Data Mining. Matrix Decompositions. Singular Value Decomposition (SVD). Graph Analysis. SemiDiscrete Decomposition (SDD). Using SVD and SDD Together. Independent Component Analysis (ICA). Non-Negative Matrix Factorization (NNMF). Tensors. Conclusion. Appendix. Bibliography. Index.

Queen's University, Kingston, Ontario, Canada

… One of this book’s attractive features is that every chapter contains a discussion relating to the algorithmic issues. One scenario is used as a running illustrative example throughout the book. Several other examples are discussed in different chapters. These examples should help the reader understand the advantages as well as the practical problems associated with any of the proposed matrix-based data mining techniques covered in the book. I recommend this book for anyone interested in using matrix methods for data mining.—Technometrics, February 2009, Vol. 51, No. 1This could be a nice companion book for courses in data mining or applied linear algebra. Producing a clear taxonomy of the use and intentions of matrix decompositions in data analysis is very useful to both students and researchers. … Those working with large-scale complex datasets will definitely find this work useful. … I would definitely use it in my own course in data mining.—Michael W. Berry, University of Tennessee, Knoxville, USA[This book] is suffused with insightful suggestions for analytical methods and interpretations, drawn from the author's own research and his reading of the literature. …The book has two great strengths. The first is its attempt to provide a unifying framework from which to view a host of important analytical methodologies based on matrix methods. … Second, the book is extremely strong on interpreting the results of matrix methods. … [It] assembles and explains a diverse set of insights that are otherwise widely scattered in the literature. This alone makes the book an important contribution to the community.—Bruce Hendrickson, Sandia National Laboratories, Albuquerque, New Mexico, USA

• Data Mining. Matrix Decompositions. Singular Value Decomposition (SVD). Graph Analysis. SemiDiscrete Decomposition (SDD). Using SVD and SDD Together. Independent Component Analysis (ICA). Non-Negative Matrix Factorization (NNMF). Tensors. Conclusion. Appendix. Bibliography. Index.