Exploratory Data Analysis with MATLAB.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface to the Third Edition -- Preface to the Second Edition -- Preface to the First Edition -- Part I: Introduction to Exploratory Data Analysis -- Chapter 1: Introduction to Exploratory Data Analysis -- 1.1 What is Exploratory Data Analysis -- 1.2 Overview of the Text -- 1.3 A Few Words about Notation -- 1.4 Data Sets Used in the Book -- 1.4.1 Unstructured Text Documents -- 1.4.2 Gene Expression Data -- 1.4.3 Oronsay Data Set -- 1.4.4 Software Inspection -- 1.5 Transforming Data -- 1.5.1 Power Transformations -- 1.5.2 Standardization -- 1.5.3 Sphering the Data -- 1.6 Further Reading -- Exercises -- Part II: EDA as Pattern Discovery -- Chapter 2: Dimensionality Reduction - Linear Methods -- 2.1 Introduction -- 2.2 Principal Component Analysis - PCA -- 2.2.1 PCA Using the Sample Covariance Matrix -- 2.2.2 PCA Using the Sample Correlation Matrix -- 2.2.3 How Many Dimensions Should We Keep -- 2.3 Singular Value Decomposition - SVD -- 2.4 Nonnegative Matrix Factorization -- 2.5 Factor Analysis -- 2.6 Fisher's Linear Discriminant -- 2.7 Random Projections -- 2.8 Intrinsic Dimensionality -- 2.8.1 Nearest Neighbor Approach -- 2.8.2 Correlation Dimension -- 2.8.3 Maximum Likelihood Approach -- 2.8.4 Estimation Using Packing Numbers -- 2.8.5 Estimation of Local Dimension -- 2.9 Summary and Further Reading -- Exercises -- Chapter 3: Dimensionality Reduction-Nonlinear Methods -- 3.1 Multidimensional Scaling - MDS -- 3.1.1 Metric MDS -- 3.1.2 Nonmetric MDS -- 3.2 Manifold Learning -- 3.2.1 Locally Linear Embedding -- 3.2.2 Isometric Feature Mapping - ISOMAP -- 3.2.3 Hessian Eigenmaps -- 3.3 Artificial Neural Network Approaches -- 3.3.1 Self-Organizing Maps -- 3.3.2 Generative Topographic Maps -- 3.3.3 Curvilinear Component Analysis -- 3.3.4 Autoencoders.

3.4 Stochastic Neighbor Embedding -- 3.5 Summary and Further Reading -- Exercises -- Chapter 4: Data Tours -- 4.1 Grand Tour -- 4.1.1 Torus Winding Method -- 4.1.2 Pseudo Grand Tour -- 4.2 Interpolation Tours -- 4.3 Projection Pursuit -- 4.4 Projection Pursuit Indexes -- 4.4.1 Posse Chi-Square Index -- 4.4.2 Moment Index -- 4.5 Independent Component Analysis -- 4.6 Summary and Further Reading -- Exercises -- Chapter 5: Finding Clusters -- 5.1 Introduction -- 5.2 Hierarchical Methods -- 5.3 Optimization Methods- k-Means -- 5.4 Spectral Clustering -- 5.5 Document Clustering -- 5.5.1 Nonnegative Matrix Factorization - Revisited -- 5.5.2 Probabilistic Latent Semantic Analysis -- 5.6 Minimum Spanning Trees and Clustering -- 5.6.1 Definitions -- 5.6.2 Minimum Spanning Tree Clustering -- 5.7 Evaluating the Clusters -- 5.7.1 Rand Index -- 5.7.2 Cophenetic Correlation -- 5.7.3 Upper Tail Rule -- 5.7.4 Silhouette Plot -- 5.7.5 Gap Statistic -- 5.7.6 Cluster Validity Indices -- 5.8 Summary and Further Reading -- Exercises -- Chapter 6: Model-Based Clustering -- 6.1 Overview of Model-Based Clustering -- 6.2 Finite Mixtures -- 6.2.1 Multivariate Finite Mixtures -- 6.2.2 Component Models - Constraining the Covariances -- 6.3 Expectation-Maximization Algorithm -- 6.4 Hierarchical Agglomerative Model-Based Clustering -- 6.5 Model-Based Clustering -- 6.6 MBC for Density Estimation and Discriminant Analysis -- 6.6.1 Introduction to Pattern Recognition -- 6.6.2 Bayes Decision Theory -- 6.6.3 Estimating Probability Densities with MBC -- 6.7 Generating Random Variables from a Mixture Model -- 6.8 Summary and Further Reading -- Exercises -- Chapter 7: Smoothing Scatterplots -- 7.1 Introduction -- 7.2 Loess -- 7.3 Robust Loess -- 7.4 Residuals and Diagnostics with Loess -- 7.4.1 Residual Plots -- 7.4.2 Spread Smooth -- 7.4.3 Loess Envelopes - Upper and Lower Smooths.

7.5 Smoothing Splines -- 7.5.1 Regression with Splines -- 7.5.2 Smoothing Splines -- 7.5.3 Smoothing Splines for Uniformly Spaced Data -- 7.6 Choosing the Smoothing Parameter -- 7.7 Bivariate Distribution Smooths -- 7.7.1 Pairs of Middle Smoothings -- 7.7.2 Polar Smoothing -- 7.8 Curve Fitting Toolbox -- 7.9 Summary and Further Reading -- Exercises -- Part III: Graphical Methods for EDA -- Chapter 8: Visualizing Clusters -- 8.1 Dendrogram -- 8.2 Treemaps -- 8.3 Rectangle Plots -- 8.4 ReClus Plots -- 8.5 Data Image -- 8.6 Summary and Further Reading -- Exercises -- Chapter 9: Distribution Shapes -- 9.1 Histograms -- 9.1.1 Univariate Histograms -- 9.1.2 Bivariate Histograms -- 9.2 Kernel Density -- 9.2.1 Univariate Kernel Density Estimation -- 9.2.2 Multivariate Kernel Density Estimation -- 9.3 Boxplots -- 9.3.1 The Basic Boxplot -- 9.3.2 Variations of the Basic Boxplot -- 9.3.3 Violin Plots -- 9.3.4 Beeswarm Plot -- 9.3.5 Beanplot -- 9.4 Quantile Plots -- 9.4.1 Probability Plots -- 9.4.2 Quantile-Quantile Plot -- 9.4.3 Quantile Plot -- 9.5 Bagplots -- 9.6 Rangefinder Boxplot -- 9.7 Summary and Further Reading -- Exercises -- Chapter 10: Multivariate Visualization -- 10.1 Glyph Plots -- 10.2 Scatterplots -- 10.2.1 2-D and 3-D Scatterplots -- 10.2.2 Scatterplot Matrices -- 10.2.3 Scatterplots with Hexagonal Binning -- 10.3 Dynamic Graphics -- 10.3.1 Identification of Data -- 10.3.2 Linking -- 10.3.3 Brushing -- 10.4 Coplots -- 10.5 Dot Charts -- 10.5.1 Basic Dot Chart -- 10.5.2 Multiway Dot Chart -- 10.6 Plotting Points as Curves -- 10.6.1 Parallel Coordinate Plots -- 10.6.2 Andrews' Curves -- 10.6.3 Andrews' Images -- 10.6.4 More Plot Matrices -- 10.7 Data Tours Revisited -- 10.7.1 Grand Tour -- 10.7.2 Permutation Tour -- 10.8 Biplots -- 10.9 Summary and Further Reading -- Exercises -- Chapter 11: Visualizing Categorical Data.

11.1 Discrete Distributions -- 11.1.1 Binomial Distribution -- 11.1.2 Poisson Distribution -- 11.2 Exploring Distribution Shapes -- 11.2.1 Poissonness Plot -- 11.2.2 Binomialness Plot -- 11.2.3 Extensions of the Poissonness Plot -- 11.2.4 Hanging Rootogram -- 11.3 Contingency Tables -- 11.3.1 Background -- 11.3.2 Bar Plots -- 11.3.3 Spine Plots -- 11.3.4 Mosaic Plots -- 11.3.5 Sieve Plots -- 11.3.6 Log Odds Plot -- 11.4 Summary and Further Reading -- Exercises -- Appendix A: Proximity Measures -- A.1 Definitions -- A.1.1 Dissimilarities -- A.1.2 Similarity Measures -- A.1.3 Similarity Measures for Binary Data -- A.1.4 Dissimilarities for Probability Density Functions -- A.2 Transformations -- A.3 Further Reading -- Appendix B: Software Resources for EDA -- B.1 MATLAB Programs -- B.2 Other Programs for EDA -- B.3 EDA Toolbox -- Appendix C: Description of Data Sets -- Appendix D: MATLAB® Basics -- D.1 Desktop Environment -- D.2 Getting Help and Other Documentation -- D.3 Data Import and Export -- D.3.1 Data Import and Export in Base MATLAB -- D.3.2 Data Import and Export with the Statistics Toolbox -- D.4 Data in MATLAB -- D.4.1 Data Objects in Base MATLAB -- D.4.2 Accessing Data Elements -- D.4.3 Object-Oriented Programming -- D.5 Workspace and Syntax -- D.5.1 File and Workspace Management -- D.5.2 Syntax in MATLAB -- D.5.3 Functions in MATLAB -- D.6 Basic Plot Functions -- D.6.1 Plotting 2D Data -- D.6.2 Plotting 3D Data -- D.6.3 Scatterplots -- D.6.4 Scatterplot Matrix -- D.6.5 GUIs for Graphics -- D.7 Summary and Further Reading -- References -- Author Index -- Subject Index.

This book describes the various methods used for exploratory data analysis with an emphasis on MATLAB implementation. It covers approaches for visualizing data, data tours and animations, clustering (or unsupervised learning), dimensionality reduction, and more. A set of graphical user interfaces allows users to apply the ideas to their own data.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.