Exploratory Data Analysis with MATLAB, Second Edition
2nd Edition
Published on 16. December 2010
Book
Hardback
536 pages
978-1-4398-1220-4 (ISBN)
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 (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
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 / Votes
"This book presents a broad panoply of data-analytical methods implemented in MATLAB. ... the amount of material covered is impressive. The explanations are clear, and the fluid style makes reading pleasant. ... very useful for the applied statistician. Its material may also be employed as a complement to a more theoretical-oriented course."-R. Maronna, Statistical Papers, Vol. 55, 2014
"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
More details
Series
Edition
2nd New edition
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Practitioners, researchers, and graduate students in statistics, engineering, and computer science.
Edition type
New edition
Product notice
Paper over boards
Illustrations
There will be an 8-page color insert containing 15 figures., 133 s/w Abbildungen, 15 farbige Abbildungen, 11 s/w Tabellen
There will be an 8-page color insert containing 15 figures.; 11 Tables, black and white; 15 Illustrations, color; 133 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
907 gr
ISBN-13
978-1-4398-1220-4 (9781439812204)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Wendy L. Martinez | Angel R. Martinez | Jeffrey Solka
Exploratory Data Analysis with MATLAB
Book
07/2017
3rd Edition
Chapman & Hall/CRC
€182.50
Shipment within 15-20 days
Previous edition
Wendy L. Martinez | Angel R. Martinez | Jeffrey Solka
Exploratory Data Analysis with MATLAB
Book
11/2004
1st Edition
Chapman & Hall/CRC
€145.58
Article exhausted; check for reprint
Persons
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.
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.
Content
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.
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.