Exploratory data analysis with MATLAB

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 MA...

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Bibliographic Details
Main Authors Martinez, Wendy L. (Author), Martinez, Angel R. (Author), Solka, Jeffrey (Author)
Format Electronic eBook
LanguageEnglish
Published Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
EditionThird edition.
SeriesSeries in computer science and data analysis.
Subjects
Online AccessFull text
ISBN9781498776073
1498776078
9781315349848
1315349841
9781523114269
1523114266
9781315366968
1315366967
9781315330815
1315330814
9781498776066
149877606X
Physical Description1 online resource (625 pages)

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100 1 |a Martinez, Wendy L.,  |e author. 
245 1 0 |a Exploratory data analysis with MATLAB /  |c Wendy L. Martinez, Angel R. Matinez, Jeffrey L. Solka. 
250 |a Third edition. 
264 1 |a Boca Raton, FL :  |b CRC Press, Taylor & Francis Group,  |c [2017] 
300 |a 1 online resource (625 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Chapman & Hall/CRC computer science and data analysis series 
505 0 |a Intro; Half Title; Series Editor; Title; Copyrights; 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. 
505 8 |a 1.5.3 Sphering the Data1.6 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. 
505 8 |a 2.8.3 Maximum Likelihood Approach2.8.4 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. 
505 8 |a 3.4 Stochastic Neighbor Embedding3.5 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. 
505 8 |a 5.5.2 Probabilistic Latent Semantic Analysis5.6 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. 
500 |a 6.4 Hierarchical Agglomerative Model-Based Clustering. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. --  |c Provided by publisher. 
504 |a Includes bibliographical references and indexes. 
590 |a Knovel  |b Knovel (All titles) 
630 0 0 |a MATLAB. 
630 0 7 |a MATLAB  |2 fast 
650 0 |a Multivariate analysis. 
650 0 |a Mathematical statistics. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Martinez, Angel R.,  |e author. 
700 1 |a Solka, Jeffrey,  |e author. 
776 0 8 |i Print version:  |a Martinez, Wendy L.  |t Exploratory data analysis with MATLAB.  |d Boca Raton : Chapman and Hall/CRC, [2017]  |z 9781498776066  |w (OCoLC)959965302 
830 0 |a Series in computer science and data analysis. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpEDAMATL3/exploratory-data-analysis?kpromoter=marc  |y Full text