Understanding complex datasets : data mining with matrix decompositions
Author(s)
Bibliographic Information
Understanding complex datasets : data mining with matrix decompositions
（Chapman & Hall/CRC data mining and knowledge discovery series）
Chapman & Hall/CRC, c2007
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Note
Includes bibliographical references (p. 223-232) and index
Description and Table of Contents
Description
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.
Table of Contents
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.
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