Exploratory Data Analysis with MATLAB

by Steven D. Strauss

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.

Hardcover

Reviews

[Jin Shusong · Rating 4 out of 5]

This book thoroughly introduced the methods used in EDA. The authors explained the theory briefly and gave the algorithms in detail. The example codes were helpful. However, I found that the author failed to explain what we could find from the results of these methods. For example, the book introduced the intrinsic dimension. The authors also gave examples. But, what could I find and how can I use these results?

[Darin]

This book discusses ways to look at data to understand the underlying structure, including quite a bit about clustering. The included Matlab code is very helpful. While this isn't really a theory book, each chapter does have a bibliography that I found useful.