Exploratory Data Analysis with MATLAB

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(R), 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

Table of Contents:
Introduction to Exploratory Data Analysis. EDA as Pattern Discovery. Graphical Methods for EDA. Appendices.

About the Author :
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.

Review :
Practitioners of EDA who use MATLAB will want a copy of this book. ! The authors discuss many EDA methods, including graphical approaches. With the book comes the EDA Toolbox (downloadable from the text website) for use with MATLAB. It contains code for all of the algorithms discussed in the text. ! the authors strategically inject helpful observations and guidance into the examples throughout the book. ! this book does not merely document routines; it shows how to do EDA. The helpful summaries, intuitive explanations, and comprehensive examples make the text so much more than a software cookbook. ! The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. This text, along with the EDA Toolbox, is an excellent resource. Even readers with limited background can quickly be analyzing data and plotting it in interesting ways. For practitioners of EDA who use MATLAB, and ideally also the Statistics Toolbox, I highly recommend this book. --MAA Reviews, April 2011 Praise for the First Edition This book ! has a good introduction to EDA, and then illustrates several applications where MATLAB provides the analysis of data to produce unexpected results. --Books-on-Line ! The audience for the book is a wide one and includes statisticians, computer scientists, and others who may be interested in or use EDA. ! I found the book to be engagingly written, and successful in its defined task of teaching the reader to use EDA with MATLAB. I liked the graphics and thought that they fully illustrated the techniques used. --Brian Jersky, Sonoma State University, Journal of the American Statistical Association The book can also be useful in a classroom setting at the senior undergraduate and graduate level, valuable exercises being included in each chapter. --Neculai Curteanu, Zentralblatt MATH