رکورد قبلیرکورد بعدی

" Exploratory data analysis with MATLAB / "


Document Type : BL
Record Number : 844000
Main Entry : Martinez, Wendy L.
Title & Author : Exploratory data analysis with MATLAB /\ Wendy L. Martinez, Angel R. Matinez, Jeffrey L. Solka.
Edition Statement : Third edition.
Publication Statement : Boca Raton, FL :: CRC Press, Taylor & Francis Group,, [2017]
Series Statement : Chapman & Hall/CRC computer science and data analysis series
Page. NO : 1 online resource (625 pages)
ISBN : 1315330814
: : 1315349841
: : 1315366967
: : 1498776078
: : 1523114266
: : 9781315330815
: : 9781315349848
: : 9781315366968
: : 9781498776073
: : 9781523114269
: 149877606X
: 9781498776066
Notes : 6.4 Hierarchical Agglomerative Model-Based Clustering.
Bibliographies/Indexes : Includes bibliographical references and indexes.
Contents : 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.
: 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.
: 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.
: 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.
: 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.
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. --
Subject : Mathematical statistics.
Subject : Multivariate analysis.
Subject : Mathematical statistics.
Subject : Multivariate analysis.
Subject : MATLAB.
: MATLAB.
Dewey Classification : ‭519.5/35028553‬
LC Classification : ‭QA278‬‭.M3735 2017eb‬
Added Entry : Martinez, Angel R.
: Solka, Jeffrey
کپی لینک

پیشنهاد خرید
پیوستها
Search result is zero
نظرسنجی
نظرسنجی منابع دیجیتال

1 - آیا از کیفیت منابع دیجیتال راضی هستید؟