Exploratory data analysis with MATLAB
Exploratory Data Analysis with MATLAB 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 e...
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| 100 | 1 | |a Martinez, Wendy L. |d 1953- |e Verfasser |0 (DE-588)1173101632 |4 aut | |
| 245 | 1 | 0 | |a Exploratory data analysis with MATLAB |c Wendy L. Martinez ; Angel R. Martinez ; Jeffrey L. Solka |
| 250 | |a 3.edition | ||
| 264 | 1 | |a Boca Raton, Fla. |b CRC Press, Chapman & Hall |c [2017] | |
| 264 | 4 | |c © 2017 | |
| 300 | |a XXV, 590 Seiten |b Illustrationen, graphische Darstellungen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Computer science and data analysis series | |
| 490 | 0 | |a A Chapman & Hall book | |
| 505 | 8 | |a This book describes the various methods used for exploratory data analysis with an emphasis on MATLAB implementation. It covers approaches for visualizing data, data tours and animations, clustering (or unsupervised learning), dimensionality reduction, and more. A set of graphical user interfaces allows users to apply the ideas to their own data. | |
| 520 | 3 | |a Exploratory Data Analysis with MATLAB 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. The revised third edition includes random projections and estimating local intrinsic dimensionality; plots for visualizing data distributions, such as beanplots and violin plots; as well as a chapter on visualizing categorical data. | |
| 630 | 0 | 4 | |a MATLAB |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 0 | 7 | |a Multivariate Analyse |0 (DE-588)4040708-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a MATLAB |0 (DE-588)4329066-8 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a MATLAB |0 (DE-588)4329066-8 |D s |
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| 700 | 1 | |a Martinez, Angel R. |e Verfasser |4 aut | |
| 700 | 1 | |a Solka, Jeffrey L. |e Verfasser |4 aut | |
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| adam_text | Table of Contents
Preface to the Third Edition...................................xvii
Preface to the Second Edition...................................xix
Preface to the First Edition..................................xxiii
Part I
Introduction to Exploratory Data Analysis
Chapter 1
Introduction to Exploratory Data Analysis
1.1 What is Exploratory Data Analysis ............................3
1.2 Overview of the Text .........................................6
1.3 A Few Words about Notation ...................................8
1.4 Data Sets Used in the Book ...................................9
1.4.1 Unstructured Text Documents ...........................9
1.4.2 Gene Expression Data .................................12
1.4.3 Oronsay Data Set .....................................18
1.4.4 Software Inspection...................................19
1.5 Transforming Data............................................20
1.5.1 Power Transformations ................................21
1.5.2 Standardization.......................................22
1.5.3 Sphering the Data ....................................24
1.6 Further Reading .............................................25
Exercises .......................................................27
Part II
EDA as Pattern Discovery
Chapter 2
Dimensionality Reduction — Linear Methods
2.1 Introduction ................................................31
2.2 Principal Component Analysis — PC A........................ 33
2.2.1 PCA Using the Sample Covariance Matrix ...............34
2.2.2 PCA Using the Sample Correlation Matrix ..............37
2.2.3 How Many Dimensions Should We Keep? ..................38
2.3 Singular Value Decomposition — SVD ..........................42
ix
x Exploratory Data Analysis with MATLAB®, Third Edition
2.4 Nonnegative Matrix Factorization ............................47
2.5 Factor Analysis .............................................51
2.6 Fisher s Linear Discriminant ................................56
2.7 Random Projections ........................................ 61
2.8 Intrinsic Dimensionality.....................................65
2.8.1 Nearest Neighbor Approach ............................67
2.8.2 Correlation Dimension ................................71
2.8.3 Maximum Likelihood Approach ..........................72
2.8.4 Estimation Using Packing Numbers ................... 74
2.8.5 Estimation of Local Dimension ........................76
2.9 Summary and Further Reading .................................79
Exercises .......................................................81
Chapter 3
Dimensionality Reduction — Nonlinear Methods
3.1 Multidimensional Scaling — MDS ..............................85
3.1.1 Metric MDS ...........................................87
3.1.2 Nonmetric MDS ........................................97
3.2 Manifold Learning ..........................................105
3.2.1 Locally Linear Embedding............’................105
3.2.2 Isometric Feature Mapping — ISOMAP ..................107
3.2.3 Hessian Eigenmaps ...................................109
3.3 Artificial Neural Network Approaches .......................114
3.3.1 Self-Organizing Maps .......................... 114
3.3.2 Generative Topographic Maps .........................117
3.3.3 Curvilinear Component Analysis ......................122
3.3.4 Autoencoders ........................................127
3.4 Stochastic Neighbor Embedding ..............................131
3.5 Summary and Further Reading ................................135
Exercises .......................................................136
Chapter 4
Data Tours
4.1 Grand Tour .................................................140
4.1.1 Torus Winding Method ................................141
4.1.2 Pseudo Grand Tour ...................................143
4.2 Interpolation Tours ........................................146
4.3 Projection Pursuit..........................................148
4.4 Projection Pursuit Indexes .................................156
4.4.1 Posse Chi-Square Index ..............................156
4.4.2 Moment Index.........................................159
4.5 Independent Component Analysis .............................161
4.6 Summary and Further Reading ................................165
Exercises ..................................................... 166
Table of Contents
xi
Chapter 5
Finding Clusters
5.1 Introduction .....................................................169
5.2 Hierarchical Methods .............................................171
5.3 Optimization Methods — /c-Means ..................................177
5.4 Spectral Clustering...............................................181
5.5 Document Clustering ..............................................185
5.5.1 Nonnegative Matrix Factorization — Revisited ..............187
5.5.2 Probabilistic Latent Semantic Analysis ....................191
5.6 Minimum Spanning Trees and Clustering.............................196
5.6.1 Definitions ...............................................196
5.6.2 Minimum Spanning Tree Clustering...........................199
5.7 Evaluating the Clusters ..........................................204
5.7.1 Rand Index ................................................205
5.7.2 Cophenetic Correlation ....................................207
5.7.3 Upper Tail Rule............................................208
5.7.4 Silhouette Plot ...........................................211
5.7.5 Gap Statistic..............................................213
5.7.6 Cluster Validity Indices ..................................219
5.8 Summary and Further Reading ......................................230
Exercises ............................................................232
Chapter 6
Model-Based Clustering
6.1 Overview of Model-Based Clustering ........................237
6.2 Finite Mixtures ...........................................240
6.2.1 Multivariate Finite Mixtures .......................242
6.2.2 Component Models — Constraining the Covariances ....243
6.3 Expectation-Maximization Algorithm ........................249
6.4 Hierarchical Agglomerative Model-Based Clustering .........254
6.5 Model-Based Clustering................................... 256
6.6 MBC for Density Estimation and Discriminant Analysis ......263
6.6.1 Introduction to Pattern Recognition ................263
6.6.2 Bayes Decision Theory...............................264
6.6.3 Estimating Probability Densities with MBC ..........267
6.7 Generating Random Variables from a Mixture Model...........271
6.8 Summary and Further Reading ...............................273
Exercises .....................................................276
Chapter 7
Smoothing Scatterplots
7.1 Introduction ...............................................279
7.2 Loess.......................................................280
7.3 Robust Loess ...............................................291
7.4 Residuals and Diagnostics with Loess .......................293
xii Exploratory Data Analysis with MATLAB®, Third Edition
7.4.1 Residual Plots......................................293
7.4.2 Spread Smooth.......................................297
7.4.3 Loess Envelopes — Upper and Lower Smooths...........300
7.5 Smoothing Splines .........................................301
7.5.1 Regression with Splines........................... 302
7.5.2 Smoothing Splines...................................304
7.5.3 Smoothing Splines for Uniformly Spaced Data ........310
7.6 Choosing the Smoothing Parameter ..........................313
7.7 Bivariate Distribution Smooths.............................317
7.7.1 Pairs of Middle Smoothings..........................317
7.7.2 Polar Smoothing ....................................319
7.8 Curve Fitting Toolbox .....................................323
7.9 Summary and Further Reading ...............................325
Exercises .....................................................326
Part III
Graphical Methods for EDA
Chapter 8
Visualizing Clusters
8.1 Dendrogram.................................................333
8.2 Treemaps ..................................................335
8.3 Rectangle Plots ...................................... ...338
8.4 ReClus Plots ..............................................344
8.5 Data Image ................................................349
8.6 Summary and Further Reading ...............................355
Exercises .....................................................356
Chapter 9
Distribution Shapes
9.1 Histograms ................................................359
9.1.1 Univariate Histograms ..............................359
9.1.2 Bivariate Histograms ...............................366
9.2 Kernel Density ............................................368
9.2.1 Univariate Kernel Density Estimation ............. 369
9.2.2 Multivariate Kernel Density Estimation .............371
9.3 Boxplots ..................................................374
9.3.1 The Basic Boxplot ..................................374
9.3.2 Variations of the Basic Boxplot.....................380
9.3.3 Violin Plots .......................................383
9.3.4 Beeswarm Plot .................................... 385
9.3.5 Beanplot ...........................................388
9.4 Quantile Plots ............................................390
9.4.1 Probability Plots ..................................392
Table of Contents
xm
9.4.2 Quantile-Quantile Plot.................................393
9.4.3 Quantile Plot .........................................397
9.5 Bagplots .....................................................399
9.6 Rangefinder Boxplot...........................................400
9.7 Summary and Further Reading ..................................405
Exercises ........................................................405
Chapter 10
Multivariate Visualization
10.1 Glyph Plots..................................................409
10.2 Scatterplots................................................ 410
10.2.1 2-D and 3-D Scatterplots .............................412
10.2.2 Scatterplot Matrices..................................415
10.2.3 Scatterplots with Hexagonal Binning...................416
10.3 Dynamic Graphics .......................................... 418
10.3.1 Identification of Data ...............................420
10.3.2 Linking ..............................................422
10.3.3 Brushing..............................................425
10.4 Coplots......................................................428
10.5 Dot Charts ..................................................431
10.5.1 Basic Dot Chart ......................................431
10.5.2 Multiway Dot Chart ...................................432
10.6 Plotting Points as Curves ...................................436
10.6.1 Parallel Coordinate Plots.............................437
10.6.2 Andrews Curves.......................................439
10.6.3 Andrews Images.......................................443
10.6.4 More Plot Matrices ...................................444
10.7 Data Tours Revisited ........................................447
10.7.1 Grand Tour ...........................................448
10.7.2 Permutation Tour .....................................449
10.8 Biplots .....................................................452
10.9 Summary and Further Reading .................................455
Exercises ........................................................457
Chapter 11
Visualizing Categorical Data
11.1 Discrete Distributions ......................................462
11.1.1 Binomial Distribution ................................462
11.1.2 Poisson Distribution..................................464
11.2 Exploring Distribution Shapes ...............................467
11.2.1 Poissonness Plot......................................467
11.2.2 Binomialness Plot ....................................469
11.2.3 Extensions of the Poissonness Plot ...................471
11.2.4 Hanging Rootogram ....................................476
11.3 Contingency Tables ..........................................479
xiv Exploratory Data Analysis with MATLAB®, Third Edition
11.3.1 Background ........................................481
11.3.2 Bar Plots .........................................483
11.3.3 Spine Plots .......................................486
11.3.4 Mosaic Plots.......................................489
11.3.5 Sieve Plots........................................490
11.3.6 Log Odds Plot .....................................493
11.4 Summary and Further Reading ..............................498
Exercises .....................................................500
Appendix A
Proximity Measures
A.l Definitions................................................503
A. 1.1 Dissimilarities ...................................504
A.1.2 Similarity Measures ................................506
A. 1.3 Similarity Measures for Binary Data ...............506
A. 1.4 Dissimilarities for Probability Density Functions .507
A.2 Transformations ......................................... 508
A. 3 Further Reading ..........................................509
Appendix B
Software Resources for EDA
B. l MATLAB Programs ..........................................511
B.2 Other Programs for EDA.....................................515
B.3 EDA Toolbox ...............................................516
Appendix C
Description of Data Sets.......................................517
Appendix D
MATLAB® Basics
D.l Desktop Environment .......................................523
D.2 Getting Help and Other Documentation.......................525
D.3 Data Import and Export ....................................526
D.3.1 Data Import and Export in Base MATLAB®..............526
D.3.2 Data Import and Export with the Statistics Toolbox..528
D.4 Data in MATLAB® ...........................................529
D.4.1 Data Objects in Base MATLAB®........................529
D.4.2 Accessing Data Elements ............................532
D.4.3 Object-Oriented Programming.........................535
D.5 Workspace and Syntax ......................................535
D.5.1 File and Workspace Management ......................536
D.5.2 Syntax in MATLAB® ..................................537
D.5.3 Functions in MATLAB®................................539
D.6 Basic Plot Functions .................................... 540
Table of Contents
xv
D.6.1 Plotting 2D Data ........................................540
D.6.2 Plotting 3D Data ........................................543
D.6.3 Scatterplots ............................................544
D.6.4 Scatterplot Matrix.......................................545
D.6.5 GUIs for Graphics .......................................545
D.7 Summary and Further Reading ....................................547
References .........................................................551
Author Index ......................................................575
Subject Index ......................................................583
Table of Contents
Preface to the Third Edition...................................xvii
Preface to the Second Edition...................................xix
Preface to the First Edition..................................xxiii
Part I
Introduction to Exploratory Data Analysis
Chapter 1
Introduction to Exploratory Data Analysis
1.1 What is Exploratory Data Analysis ............................3
1.2 Overview of the Text .........................................6
1.3 A Few Words about Notation ...................................8
1.4 Data Sets Used in the Book ...................................9
1.4.1 Unstructured Text Documents ...........................9
1.4.2 Gene Expression Data .................................12
1.4.3 Oronsay Data Set .....................................18
1.4.4 Software Inspection...................................19
1.5 Transforming Data............................................20
1.5.1 Power Transformations ................................21
1.5.2 Standardization.......................................22
1.5.3 Sphering the Data ....................................24
1.6 Further Reading .............................................25
Exercises .......................................................27
Part II
EDA as Pattern Discovery
Chapter 2
Dimensionality Reduction — Linear Methods
2.1 Introduction ................................................31
2.2 Principal Component Analysis — PC A........................ 33
2.2.1 PCA Using the Sample Covariance Matrix ...............34
2.2.2 PCA Using the Sample Correlation Matrix ..............37
2.2.3 How Many Dimensions Should We Keep? ..................38
2.3 Singular Value Decomposition — SVD ..........................42
ix
x Exploratory Data Analysis with MATLAB®, Third Edition
2.4 Nonnegative Matrix Factorization ............................47
2.5 Factor Analysis .............................................51
2.6 Fisher s Linear Discriminant ................................56
2.7 Random Projections ........................................ 61
2.8 Intrinsic Dimensionality.....................................65
2.8.1 Nearest Neighbor Approach ............................67
2.8.2 Correlation Dimension ................................71
2.8.3 Maximum Likelihood Approach ..........................72
2.8.4 Estimation Using Packing Numbers ................... 74
2.8.5 Estimation of Local Dimension ........................76
2.9 Summary and Further Reading .................................79
Exercises .......................................................81
Chapter 3
Dimensionality Reduction — Nonlinear Methods
3.1 Multidimensional Scaling — MDS ..............................85
3.1.1 Metric MDS ...........................................87
3.1.2 Nonmetric MDS ........................................97
3.2 Manifold Learning ..........................................105
3.2.1 Locally Linear Embedding............’................105
3.2.2 Isometric Feature Mapping — ISOMAP ..................107
3.2.3 Hessian Eigenmaps ...................................109
3.3 Artificial Neural Network Approaches .......................114
3.3.1 Self-Organizing Maps .......................... 114
3.3.2 Generative Topographic Maps .........................117
3.3.3 Curvilinear Component Analysis ......................122
3.3.4 Autoencoders ........................................127
3.4 Stochastic Neighbor Embedding ..............................131
3.5 Summary and Further Reading ................................135
Exercises .......................................................136
Chapter 4
Data Tours
4.1 Grand Tour .................................................140
4.1.1 Torus Winding Method ................................141
4.1.2 Pseudo Grand Tour ...................................143
4.2 Interpolation Tours ........................................146
4.3 Projection Pursuit..........................................148
4.4 Projection Pursuit Indexes .................................156
4.4.1 Posse Chi-Square Index ..............................156
4.4.2 Moment Index.........................................159
4.5 Independent Component Analysis .............................161
4.6 Summary and Further Reading ................................165
Exercises ..................................................... 166
Table of Contents
xi
Chapter 5
Finding Clusters
5.1 Introduction .....................................................169
5.2 Hierarchical Methods .............................................171
5.3 Optimization Methods — /c-Means ..................................177
5.4 Spectral Clustering...............................................181
5.5 Document Clustering ..............................................185
5.5.1 Nonnegative Matrix Factorization — Revisited ..............187
5.5.2 Probabilistic Latent Semantic Analysis ....................191
5.6 Minimum Spanning Trees and Clustering.............................196
5.6.1 Definitions ...............................................196
5.6.2 Minimum Spanning Tree Clustering...........................199
5.7 Evaluating the Clusters ..........................................204
5.7.1 Rand Index ................................................205
5.7.2 Cophenetic Correlation ....................................207
5.7.3 Upper Tail Rule............................................208
5.7.4 Silhouette Plot ...........................................211
5.7.5 Gap Statistic..............................................213
5.7.6 Cluster Validity Indices ..................................219
5.8 Summary and Further Reading ......................................230
Exercises ............................................................232
Chapter 6
Model-Based Clustering
6.1 Overview of Model-Based Clustering ........................237
6.2 Finite Mixtures ...........................................240
6.2.1 Multivariate Finite Mixtures .......................242
6.2.2 Component Models — Constraining the Covariances ....243
6.3 Expectation-Maximization Algorithm ........................249
6.4 Hierarchical Agglomerative Model-Based Clustering .........254
6.5 Model-Based Clustering................................... 256
6.6 MBC for Density Estimation and Discriminant Analysis ......263
6.6.1 Introduction to Pattern Recognition ................263
6.6.2 Bayes Decision Theory...............................264
6.6.3 Estimating Probability Densities with MBC ..........267
6.7 Generating Random Variables from a Mixture Model...........271
6.8 Summary and Further Reading ...............................273
Exercises .....................................................276
Chapter 7
Smoothing Scatterplots
7.1 Introduction ...............................................279
7.2 Loess.......................................................280
7.3 Robust Loess ...............................................291
7.4 Residuals and Diagnostics with Loess .......................293
xii Exploratory Data Analysis with MATLAB®, Third Edition
7.4.1 Residual Plots......................................293
7.4.2 Spread Smooth.......................................297
7.4.3 Loess Envelopes — Upper and Lower Smooths...........300
7.5 Smoothing Splines .........................................301
7.5.1 Regression with Splines........................... 302
7.5.2 Smoothing Splines...................................304
7.5.3 Smoothing Splines for Uniformly Spaced Data ........310
7.6 Choosing the Smoothing Parameter ..........................313
7.7 Bivariate Distribution Smooths.............................317
7.7.1 Pairs of Middle Smoothings..........................317
7.7.2 Polar Smoothing ....................................319
7.8 Curve Fitting Toolbox .....................................323
7.9 Summary and Further Reading ...............................325
Exercises .....................................................326
Part III
Graphical Methods for EDA
Chapter 8
Visualizing Clusters
8.1 Dendrogram.................................................333
8.2 Treemaps ..................................................335
8.3 Rectangle Plots ...................................... ...338
8.4 ReClus Plots ..............................................344
8.5 Data Image ................................................349
8.6 Summary and Further Reading ...............................355
Exercises .....................................................356
Chapter 9
Distribution Shapes
9.1 Histograms ................................................359
9.1.1 Univariate Histograms ..............................359
9.1.2 Bivariate Histograms ...............................366
9.2 Kernel Density ............................................368
9.2.1 Univariate Kernel Density Estimation ............. 369
9.2.2 Multivariate Kernel Density Estimation .............371
9.3 Boxplots ..................................................374
9.3.1 The Basic Boxplot ..................................374
9.3.2 Variations of the Basic Boxplot.....................380
9.3.3 Violin Plots .......................................383
9.3.4 Beeswarm Plot .................................... 385
9.3.5 Beanplot ...........................................388
9.4 Quantile Plots ............................................390
9.4.1 Probability Plots ..................................392
Table of Contents
xm
9.4.2 Quantile-Quantile Plot.................................393
9.4.3 Quantile Plot .........................................397
9.5 Bagplots .....................................................399
9.6 Rangefinder Boxplot...........................................400
9.7 Summary and Further Reading ..................................405
Exercises ........................................................405
Chapter 10
Multivariate Visualization
10.1 Glyph Plots..................................................409
10.2 Scatterplots................................................ 410
10.2.1 2-D and 3-D Scatterplots .............................412
10.2.2 Scatterplot Matrices..................................415
10.2.3 Scatterplots with Hexagonal Binning...................416
10.3 Dynamic Graphics .......................................... 418
10.3.1 Identification of Data ...............................420
10.3.2 Linking ..............................................422
10.3.3 Brushing..............................................425
10.4 Coplots......................................................428
10.5 Dot Charts ..................................................431
10.5.1 Basic Dot Chart ......................................431
10.5.2 Multiway Dot Chart ...................................432
10.6 Plotting Points as Curves ...................................436
10.6.1 Parallel Coordinate Plots.............................437
10.6.2 Andrews Curves.......................................439
10.6.3 Andrews Images.......................................443
10.6.4 More Plot Matrices ...................................444
10.7 Data Tours Revisited ........................................447
10.7.1 Grand Tour ...........................................448
10.7.2 Permutation Tour .....................................449
10.8 Biplots .....................................................452
10.9 Summary and Further Reading .................................455
Exercises ........................................................457
Chapter 11
Visualizing Categorical Data
11.1 Discrete Distributions ......................................462
11.1.1 Binomial Distribution ................................462
11.1.2 Poisson Distribution..................................464
11.2 Exploring Distribution Shapes ...............................467
11.2.1 Poissonness Plot......................................467
11.2.2 Binomialness Plot ....................................469
11.2.3 Extensions of the Poissonness Plot ...................471
11.2.4 Hanging Rootogram ....................................476
11.3 Contingency Tables ..........................................479
xiv Exploratory Data Analysis with MATLAB®, Third Edition
11.3.1 Background ........................................481
11.3.2 Bar Plots .........................................483
11.3.3 Spine Plots .......................................486
11.3.4 Mosaic Plots.......................................489
11.3.5 Sieve Plots........................................490
11.3.6 Log Odds Plot .....................................493
11.4 Summary and Further Reading ..............................498
Exercises .....................................................500
Appendix A
Proximity Measures
A.l Definitions................................................503
A. 1.1 Dissimilarities ...................................504
A.1.2 Similarity Measures ................................506
A. 1.3 Similarity Measures for Binary Data ...............506
A. 1.4 Dissimilarities for Probability Density Functions .507
A.2 Transformations ......................................... 508
A. 3 Further Reading ..........................................509
Appendix B
Software Resources for EDA
B. l MATLAB Programs ..........................................511
B.2 Other Programs for EDA.....................................515
B.3 EDA Toolbox ...............................................516
Appendix C
Description of Data Sets.......................................517
Appendix D
MATLAB® Basics
D.l Desktop Environment .......................................523
D.2 Getting Help and Other Documentation.......................525
D.3 Data Import and Export ....................................526
D.3.1 Data Import and Export in Base MATLAB®..............526
D.3.2 Data Import and Export with the Statistics Toolbox..528
D.4 Data in MATLAB® ...........................................529
D.4.1 Data Objects in Base MATLAB®........................529
D.4.2 Accessing Data Elements ............................532
D.4.3 Object-Oriented Programming.........................535
D.5 Workspace and Syntax ......................................535
D.5.1 File and Workspace Management ......................536
D.5.2 Syntax in MATLAB® ..................................537
D.5.3 Functions in MATLAB®................................539
D.6 Basic Plot Functions .................................... 540
Table of Contents
xv
D.6.1 Plotting 2D Data ........................................540
D.6.2 Plotting 3D Data ........................................543
D.6.3 Scatterplots ............................................544
D.6.4 Scatterplot Matrix.......................................545
D.6.5 GUIs for Graphics .......................................545
D.7 Summary and Further Reading ....................................547
References .........................................................551
Author Index ......................................................575
Subject Index ......................................................583
|
| any_adam_object | 1 |
| author | Martinez, Wendy L. 1953- Martinez, Angel R. Solka, Jeffrey L. |
| author_GND | (DE-588)1173101632 |
| author_facet | Martinez, Wendy L. 1953- Martinez, Angel R. Solka, Jeffrey L. |
| author_role | aut aut aut |
| author_sort | Martinez, Wendy L. 1953- |
| author_variant | w l m wl wlm a r m ar arm j l s jl jls |
| building | Verbundindex |
| bvnumber | BV043874339 |
| classification_rvk | SK 830 ST 601 |
| contents | This book describes the various methods used for exploratory data analysis with an emphasis on MATLAB implementation. It covers approaches for visualizing data, data tours and animations, clustering (or unsupervised learning), dimensionality reduction, and more. A set of graphical user interfaces allows users to apply the ideas to their own data. |
| ctrlnum | (OCoLC)989127657 (DE-599)BVBBV043874339 |
| discipline | Informatik Mathematik |
| edition | 3.edition |
| format | Book |
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| id | DE-604.BV043874339 |
| illustrated | Illustrated |
| index_date | 2024-09-20T13:29:36Z |
| indexdate | 2024-09-27T16:41:23Z |
| institution | BVB |
| isbn | 9781498776066 9781032179056 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029284122 |
| oclc_num | 989127657 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-29T DE-706 DE-739 DE-19 DE-BY-UBM DE-20 DE-188 |
| owner_facet | DE-473 DE-BY-UBG DE-29T DE-706 DE-739 DE-19 DE-BY-UBM DE-20 DE-188 |
| physical | XXV, 590 Seiten Illustrationen, graphische Darstellungen |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press, Chapman & Hall |
| record_format | marc |
| series2 | Computer science and data analysis series A Chapman & Hall book |
| spellingShingle | Martinez, Wendy L. 1953- Martinez, Angel R. Solka, Jeffrey L. Exploratory data analysis with MATLAB This book describes the various methods used for exploratory data analysis with an emphasis on MATLAB implementation. It covers approaches for visualizing data, data tours and animations, clustering (or unsupervised learning), dimensionality reduction, and more. A set of graphical user interfaces allows users to apply the ideas to their own data. MATLAB Multivariate analysis Mathematical statistics Multivariate Analyse (DE-588)4040708-1 gnd MATLAB (DE-588)4329066-8 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4329066-8 |
| title | Exploratory data analysis with MATLAB |
| title_auth | Exploratory data analysis with MATLAB |
| title_exact_search | Exploratory data analysis with MATLAB |
| title_full | Exploratory data analysis with MATLAB Wendy L. Martinez ; Angel R. Martinez ; Jeffrey L. Solka |
| title_fullStr | Exploratory data analysis with MATLAB Wendy L. Martinez ; Angel R. Martinez ; Jeffrey L. Solka |
| title_full_unstemmed | Exploratory data analysis with MATLAB Wendy L. Martinez ; Angel R. Martinez ; Jeffrey L. Solka |
| title_short | Exploratory data analysis with MATLAB |
| title_sort | exploratory data analysis with matlab |
| topic | MATLAB Multivariate analysis Mathematical statistics Multivariate Analyse (DE-588)4040708-1 gnd MATLAB (DE-588)4329066-8 gnd |
| topic_facet | MATLAB Multivariate analysis Mathematical statistics Multivariate Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029284122&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029284122&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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