عنوان
پدید آورنده
موضوع
رده
QA278 .M3735 2017eb
QA278
.
M3735
2017eb
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1315330814
(Number (ISBN
1315349841
(Number (ISBN
1315366967
(Number (ISBN
1498776078
(Number (ISBN
1523114266
(Number (ISBN
9781315330815
(Number (ISBN
9781315349848
(Number (ISBN
9781315366968
(Number (ISBN
9781498776073
(Number (ISBN
9781523114269
Erroneous ISBN
149877606X
Erroneous ISBN
9781498776066
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Exploratory data analysis with MATLAB /
General Material Designation
[Book]
First Statement of Responsibility
Wendy L. Martinez, Angel R. Matinez, Jeffrey L. Solka.
EDITION STATEMENT
Edition Statement
Third edition.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, FL :
Name of Publisher, Distributor, etc.
CRC Press, Taylor & Francis Group,
Date of Publication, Distribution, etc.
[2017]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (625 pages)
SERIES
Series Title
Chapman & Hall/CRC computer science and data analysis series
GENERAL NOTES
Text of Note
6.4 Hierarchical Agglomerative Model-Based Clustering.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and indexes.
CONTENTS NOTE
Text of Note
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.
Text of Note
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.
Text of Note
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.
Text of Note
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.
Text of Note
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.
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SUMMARY OR ABSTRACT
Text of Note
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. --
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Ingram Content Group
Stock Number
9781315349848
OTHER EDITION IN ANOTHER MEDIUM
Title
Exploratory data analysis with MATLAB.
International Standard Book Number
9781498776066
TITLE USED AS SUBJECT
MATLAB.
MATLAB.
TOPICAL NAME USED AS SUBJECT
Mathematical statistics.
Multivariate analysis.
Mathematical statistics.
Multivariate analysis.
DEWEY DECIMAL CLASSIFICATION
Number
519
.
5/35028553
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA278
Book number
.
M3735
2017eb
PERSONAL NAME - PRIMARY RESPONSIBILITY
Martinez, Wendy L.
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Martinez, Angel R.
Solka, Jeffrey
ORIGINATING SOURCE
Date of Transaction
20200822171841.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
