Exploratory Data Analysis with MATLAB, Second Edition

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

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.