Exploratory Data Analysis with MATLAB, Second Edition (Chapman & Hall CRC Computer Science & Data Analysis)

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 From The First Edition...exploratory Data Analysis (eda) Was Conceived At A Time When Computers Were Not Widely Used, And Thus Computational Ability Was Rather Limited. As Computational Sophistication Has Increased, Eda Has Become An Even More Powerful Process For Visualizing And Summarizing Data Before Making Model Assumptions To Generate Hypotheses, Encompassing Larger And More Complex Data Sets. There Are Many Resources For Those Interested In The Theory Of Eda, But This Is The First Book To Use Matlab To Illustrate The Computational Aspects Of This Discipline.exploratory Data Analysis With Matlab Presents The Methods Of Eda From A Computational Perspective. The Authors Extensively Use Matlab Code And Algorithm Descriptions To Provide State-of-the-art Techniques For Finding Patterns And Structure In Data. Addressing Theory, They Also Incorporate Many Annotated References To Direct Readers To The More Theoretical Aspects Of The Methods. The Book Presents An Approach Using The Basic Functions From Matlab And The Matlab Statistics Toolbox, In Order To Be More Accessible And Enduring. It Also Contains Pseudo-code To Enable Users Of Other Software Packages To Implement The Algorithms.

this Text Places The Tools Needed To Implement Eda Theory At The Fingertips Of Researchers, Applied Mathematicians, Computer Scientists, Engineers, And Statisticians By Using A Practical/computational Approach-- Using Matlabʼ To Illustrate Computational Aspects Of Eda, This Second Edition Updates All The Techniques And Improves The Toolboxes In Each Chapter. The Authors Extensively Use Matlab Code And Algorithm Descriptions To Provide State-of-the-art Techniques For Finding Patterns And Structure In Data. Addressing Theory, They Also Incorporate Many Annotated References To Direct Readers To The More Theoretical Aspects Of The Methods. The Book Presents An Approach Using Only The Basic Functions From Matlab And The Matlab Statistics Toolbox In Order To Be More Accessible And Enduring. It Also Contains Pseudo-code To Enable Users Of Other Software Packages To Implement The Algorithms-- Pt. 1. Introduction To Exploratory Data Analysis -- Pt. 2. Eda As Pattern Discovery -- Pt. 3. Graphical Methods For Eda. Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka. Includes Bibliographical References (p. 475-495) And Indexes. "Using MATLABʼ to illustrate computational aspects of EDA, this second edition updates all the techniques and improves the Toolboxes in each chapter. The authors extensively use MATLAB code and algorithm descriptions to provide state-of-the-art techniques for finding patterns and structure in data. Addressing theory, they also incorporate many annotated references to direct readers to the more theoretical aspects of the methods. The book presents an approach using only the basic functions from MATLAB and the MATLAB Statistics Toolbox in order to be more accessible and enduring. It also contains pseudo-code to enable users of other software packages to implement the algorithms"-- Provided by publisher