Exploratory Data Analysis with MATLAB, Second Edition (Chapman & Hall/CRC Computer Science & Data Analysis)

Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB^®, Second Edition uses numerous examples and applications to show how the methods are used in practice.

New to the Second Edition

• Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines
• An expanded set of methods for estimating the intrinsic dimensionality of a data set
• Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering
• Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images
• Instructions on a free MATLAB GUI toolbox for EDA

Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.info

Wendy L. Martinez has been in government service for over 20 years, working with leading researchers from academia, industry, and government labs. During this time, she has conducted and published research in text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition. A fellow of the American Statistical Association, she earned an M.S. in aerospace engineering from George Washington University and a Ph.D. in computational sciences and informatics from George Mason University.

Angel R. Martinez teaches undergraduate and graduate courses in statistics and mathematics at Strayer University. Before retiring from government service, he worked for the U.S. Navy as an operations research analyst and a computer scientist. He earned an M.S. in systems engineering from the Virginia Polytechnic Institute and State University and a Ph.D. in computational sciences and informatics from George Mason University.

Since 1984, Jeffrey L. Solka has been working in statistical pattern recognition for the Department of the Navy. He has published over 120 journal, conference, and technical papers; has won numerous awards; and holds 4 patents. He earned an M.S. in mathematics from James Madison University, an M.S. in physics from Virginia Polytechnic Institute and State University, and a Ph.D. in computational sciences and informatics from George Mason University.