Exploratory data analysis with MATLAB

Description

Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website. -- Abstract: Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website Content: Part IIntroduction to Exploratory Data AnalysisWhat is Exploratory Data Analysis Overview of the Text A Few Words about Notation Data Sets Used in the Book Unstructured Text Documents Gene Expression Data Oronsay Data Set Software Inspection Transforming Data Power Transformations Standardization Sphering the Data Further Reading Exercises Part IIEDA as Pattern DiscoveryDimensionality Reduction - Linear MethodsIntroduction Principal Component Analysis - PCA PCA Using the Sample Covariance Matrix PCA Using the Sample Correlation Matrix How Many Dimensions Should We Keep? Singular Value Decomposition - SVD Nonnegative Matrix Factorization Factor Analysis Fisher's Linear Discriminant Random Projections Intrinsic Dimensionality Nearest Neighbor Approach Correlation Dimension Maximum Likelihood Approach Estimation Using Packing Numbers Estimation of Local Dimension Summary and Further Reading Exercises Dimensionality Reduction - Nonlinear MethodsMultidimensional Scaling - MDS Metric MDS Nonmetric MDS Manifold Learning Locally Linear Embedding Isometric Feature Mapping - ISOMAP Hessian Eigenmaps Artificial Neural Network Approaches Self-Organizing Maps Generative Topographic Maps Curvilinear Component Analysis Autoencoders Stochastic Neighbor Embedding Summary and Further Reading Exercises Data ToursGrand Tour Torus Winding Method Pseudo Grand Tour Interpolation Tours Projection Pursuit Projection Pursuit Indexes Posse Chi-Square Index Moment Index Independent Component Analysis Summary and Further Reading Exercises Finding ClustersIntroduction Hierarchical Methods Optimization Methods - k-Means Spectral Clustering Document Clustering Nonnegative Matrix Factorization - Revisited Probabilistic Latent Semantic Analysis Minimal Spanning Trees and Clustering Definitions Minimum Spanning Tree Clustering Evaluating the Clusters Rand Index Cophenetic Correlation Upper Tail Rule Silhouette Plot Gap Statistic Cluster Validity Indices Summary and Further Reading Exercises Model-Based ClusteringOverview of Model-Based Clustering Finite Mixtures Multivariate Finite Mixtures Component Models - Constraining the Covariances Expectation-Maximization Algorithm Hierarchical Agglomerative Model-Based Clustering Model-Based Clustering MBC for Density Estimation and Discriminant Analysis Introduction to Pattern Recognition Bayes Decision Theory Estimating Probability Densities with MBC Generating Random Variables from a Mixture Model Summary and Further Reading Exercises Smoothing ScatterplotsIntroduction Loess Robust Loess Residuals and Diagnostics with Loess Residual Plots Spread Smooth Loess Envelopes - Upper and Lower Smooths Smoothing Splines Regression with Splines Smoothing Splines Smoothing Splines for Uniformly Spaced Data Choosing the Smoothing Parameter Bivariate Distribution Smooths Pairs of Middle Smoothings Polar Smoothing Curve Fitting Toolbox Summary and Further Reading Exercises Part IIIGraphical Methods for EDAVisualizing ClustersDendrogram Treemaps Rectangle Plots ReClus Plots Data Image Summary and Further Reading Exercises Distribution ShapesHistograms Univariate Histograms Bivariate Histograms Kernel Density Univariate Kernel Density Estimation Multivariate Kernel Density Estimation Boxplots The Basic Boxplot Variations of the Basic Boxplot Violin Plots Beeswarm Plot Bean Plot Quantile Plots Probability Plots Quantile-Quantile Plot Quantile Plot Bagplots Rangefinder Boxplot Summary and Further Reading Exercises Multivariate VisualizationGlyph Plots Scatterplots 2-D and 3-D Scatterplots Scatterplot Matrices Scatterplots with Hexagonal Binning Dynamic Graphics Identification of Data Linking Brushing Coplots Dot Charts Basic Dot Chart Multiway Dot Chart Plotting Points as Curves Parallel Coordinate Plots Andrews' Curves Andrews' Images More Plot Matrices Data Tours Revisited Grand Tour Permutation Tour Biplots Summary and Further Reading Exercises Visualizing Categorical DataDiscrete Distributions Binomial Distribution Poisson Distribution Exploring Distribution Shapes Poissonness Plot Binomialness Plot Hanging Rootogram Contingency Tables Background Bar Plots Spine Plots Mosaic Plots Sieve Diagrams Log Odds Plot Summary and Further Reading Exercises Appendix AProximity MeasuresAppendix BSoftware Resources for EDAAppendix CAppendix DMATLAB (R) Basics