Exploratory data analysis with MATLAB(R)
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Bibliographic Information
Exploratory data analysis with MATLAB(R)
(Series in computer science and data analysis)
CRC Press, c2011
2nd ed
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Note
Bibliography: p. 475-495
Includes indexes
Description and Table of Contents
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
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.
by "Nielsen BookData"