Exploratory data analysis with MATLAB
 Responsibility
 Wendy L. Martinez, Angel R. Matinez, Jeffrey L. Solka.
 Edition
 Third edition.
 Publication
 Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
 Copyright notice
 ©2017
 Physical description
 xxv, 590 pages ; 24 cm.
 Series
 Series in computer science and data analysis.
At the library
Science Library (Li and Ma)
Stacks
Call number  Status 

QA278 .M3735 2017  Unknown 
More options
Description
Creators/Contributors
 Author/Creator
 Martinez, Wendy L., author.
 Contributor
 Martinez, Angel R., author.
 Solka, Jeffrey L., 1955 author.
Contents/Summary
 Bibliography
 Includes bibliographical references and indexes.
 Contents

 Part I 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 Unstructured Text Documents Gene Expression Data Oronsay Data Set Software Inspection Transforming Data Power Transformations Standardization Sphering the Data Further Reading Exercises
 Part II EDA as Pattern Discovery Dimensionality Reduction  Linear Methods Introduction 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 Methods Multidimensional Scaling  MDS Metric MDS Nonmetric MDS Manifold Learning Locally Linear Embedding Isometric Feature Mapping  ISOMAP Hessian Eigenmaps Artificial Neural Network Approaches SelfOrganizing Maps Generative Topographic Maps Curvilinear Component Analysis Autoencoders Stochastic Neighbor Embedding Summary and Further Reading Exercises
 Data Tours Grand Tour Torus Winding Method Pseudo Grand Tour Interpolation Tours Projection Pursuit Projection Pursuit Indexes Posse ChiSquare Index Moment Index Independent Component Analysis Summary and Further Reading Exercises
 Finding Clusters Introduction Hierarchical Methods Optimization Methods  kMeans 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
 ModelBased Clustering Overview of ModelBased Clustering Finite Mixtures Multivariate Finite Mixtures Component Models  Constraining the Covariances ExpectationMaximization Algorithm Hierarchical Agglomerative ModelBased Clustering ModelBased 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 Scatterplots Introduction 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 III Graphical Methods for EDA Visualizing Clusters Dendrogram Treemaps Rectangle Plots ReClus Plots Data Image Summary and Further Reading Exercises
 Distribution Shapes Histograms 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 QuantileQuantile Plot Quantile Plot Bagplots Rangefinder Boxplot Summary and Further Reading Exercises
 Multivariate Visualization Glyph Plots Scatterplots 2D and 3D 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 Data Discrete 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 A Proximity Measures Appendix B Software Resources for EDA
 Appendix C Appendix D MATLAB(R) Basics.
 (source: Nielsen Book Data)
 Publisher's Summary
 ["Praise for the Second Edition: \"The authors present an intuitive and easytoread book...accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB.\" Adolfo Alvarez Pinto, International Statistical Review \"Practitioners of EDA who use MATLAB will want a copy of this book...The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. 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, pseudocode, 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. New to the Third Edition * Random projections and estimating local intrinsic dimensionality * Deep learning autoencoders and stochastic neighbor embedding * Minimum spanning tree and additional cluster validity indices * Kernel density estimation * Plots for visualizing data distributions, such as beanplots and violin plots * A chapter on visualizing categorical data.", {"source"=>"(source: Nielsen Book Data)"}, "9781498776066", "20171009"]
Subjects
Bibliographic information
 Publication date
 2017
 Copyright date
 2017
 Series
 Chapman & Hall/CRC computer science and data analysis series
 ISBN
 9781498776066 hardcover
 149877606X hardcover