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Exploratory Data Analysis with MATLAB, Second Edition

By Wendy L. Martinez, Angel Martinez, Jeffrey Solka

CRC Press – 2010 – 530 pages

Series: Chapman & Hall/CRC Computer Science & Data Analysis

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    978-1-43-981220-4
    December 16th 2010

Description

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

Reviews

"The book is very helpful for applied data analysts as an excellent compact overview of popular available methods supplied with a MATLAB code. … Common features and differences between various methods are carefully explained and the book is well understandable from the perspective of the users. … The book, written by very experienced authors, can be strongly recommended as an excellent manual for MATLAB users who need to extract information from their data."

—Jan Kalina, ISCB Newsletter, June 2013

"The authors present an intuitive and easy-to-read book. … accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB. … a great contribution to the field of data analysis, which I am sure will be useful for researchers and practitioners."

—Adolfo Alvarez Pinto, International Statistical Review (2011), 79

"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, 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

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.

Author Bio

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.

Name: Exploratory Data Analysis with MATLAB, Second Edition (Hardback)CRC Press 
Description: By Wendy L. Martinez, Angel Martinez, Jeffrey Solka. 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...
Categories: Mathematics & Statistics for Engineers, Statistics & Computing, Statistical Computing