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
Introduction to Exploratory Data Analysis
What Is Exploratory Data Analysis
Overview of the Text
A Few Words about Notation
Data Sets Used in the Book
Transforming Data
EDA AS PATTERN DISCOVERY
Dimensionality Reduction - Linear Methods
Introduction
Principal Component Analysis (PCA)
Singular Value Decomposition (SVD)
Nonnegative Matrix Factorization
Factor Analysis
Fisher’s Linear Discriminant
Intrinsic Dimensionality
Dimensionality Reduction - Nonlinear Methods
Multidimensional Scaling (MDS)
Manifold Learning
Artificial Neural Network Approaches
Data Tours
Grand Tour
Interpolation Tours
Projection Pursuit
Projection Pursuit Indexes
Independent Component Analysis
Finding Clusters
Introduction
Hierarchical Methods
Optimization Methods—k-Means
Spectral Clustering
Document Clustering
Evaluating the Clusters
Model-Based Clustering
Overview of Model-Based Clustering
Finite Mixtures
Expectation-Maximization Algorithm
Hierarchical Agglomerative Model-Based Clustering
Model-Based Clustering
MBC for Density Estimation and Discriminant Analysis
Generating Random Variables from a Mixture Model
Smoothing Scatterplots
Introduction
Loess
Robust Loess
Residuals and Diagnostics with Loess
Smoothing Splines
Choosing the Smoothing Parameter
Bivariate Distribution Smooths
Curve Fitting Toolbox
GRAPHICAL METHODS FOR EDA
Visualizing Clusters
Dendrogram
Treemaps
Rectangle Plots
ReClus Plots
Data Image
Distribution Shapes
Histograms
Boxplots
Quantile Plots
Bagplots
Rangefinder Boxplot
Multivariate Visualization
Glyph Plots
Scatterplots
Dynamic Graphics
Coplots
Dot Charts
Plotting Points as Curves
Data Tours Revisited
Biplots
Appendix A: Proximity Measures
Appendix B: Software Resources for EDA
Appendix C: Description of Data Sets
Appendix D: Introduction to MATLAB
Appendix E: MATLAB Functions
References
Index
Summary, Further Reading, and Exercises appear at the end of each chapter.
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
