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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press, Taylor & Francis Group,
[2017]
|
| Edición: | Third edition. |
| Colección: | Series in computer science and data analysis.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 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 | |
| 588 | 0 | |a Print version record. | |
| 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. | ||
| 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 ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 630 | 0 | 0 | |a MATLAB. |
| 630 | 0 | 7 | |a MATLAB |2 fast |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 2 | |a Multivariate Analysis | |
| 650 | 6 | |a Analyse multivariée. | |
| 650 | 7 | |a Mathematical statistics |2 fast | |
| 650 | 7 | |a Multivariate analysis |2 fast | |
| 700 | 1 | |a Martinez, Angel R., |e author. | |
| 700 | 1 | |a Solka, Jeffrey, |e author. | |
| 758 | |i has work: |a Exploratory data analysis with MATLAB (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFtGYgxqRdCFXTCT66cy7d |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 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://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5475665 |z Texto completo |
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