Exploratory Data Analysis with MATLAB
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 29. November 2004
Book
Hardback
424 pages
978-1-58488-366-1 (ISBN)
Article exhausted; check for reprint
Description
Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger and more complex data sets. There are many resources for those interested in the theory of EDA, but this is the first book to use MATLAB to illustrate the computational aspects of this discipline.
Exploratory Data Analysis with MATLAB presents the methods of EDA from a computational perspective. The authors extensively use MATLAB code and algorithm descriptions to provide state-of-the-art techniques for finding patterns and structure in data. Addressing theory, they also incorporate many annotated references to direct readers to the more theoretical aspects of the methods. The book presents an approach using the basic functions from MATLAB and the MATLAB Statistics Toolbox, in order to be more accessible and enduring. It also contains pseudo-code to enable users of other software packages to implement the algorithms.
This text places the tools needed to implement EDA theory at the fingertips of researchers, applied mathematicians, computer scientists, engineers, and statisticians by using a practical/computational approach.
Exploratory Data Analysis with MATLAB presents the methods of EDA from a computational perspective. The authors extensively use MATLAB code and algorithm descriptions to provide state-of-the-art techniques for finding patterns and structure in data. Addressing theory, they also incorporate many annotated references to direct readers to the more theoretical aspects of the methods. The book presents an approach using the basic functions from MATLAB and the MATLAB Statistics Toolbox, in order to be more accessible and enduring. It also contains pseudo-code to enable users of other software packages to implement the algorithms.
This text places the tools needed to implement EDA theory at the fingertips of researchers, applied mathematicians, computer scientists, engineers, and statisticians by using a practical/computational approach.
More details
Series
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Statisticians, applied mathematicians, scientists, computer scientists, engineers, social scientists, and students in these fields
Illustrations
100 s/w Abbildungen, 14 s/w Tabellen
14 Tables, black and white; 100 Illustrations, black and white
Dimensions
Height: 235 mm
Width: 156 mm
Weight
726 gr
ISBN-13
978-1-58488-366-1 (9781584883661)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

Wendy L. Martinez | Angel R. Martinez | Jeffrey Solka
Exploratory Data Analysis with MATLAB, Second Edition
Book
12/2010
2nd Edition
CRC Press
€94.08
Article exhausted; check for reprint
Persons
Author
U.S. Bureau of Labor Statistics, Washington, DC, USA
U.S. Bureau of Labor Statistics, Washington, DC, USA
Bureau of Labor Statistics
Strayer University, Fredericksburg, Virginia, USA
Content
INTRODUCTION TO EXPLORATORY DATA ANALYSIS
Introduction to Exploratory Data Analysis
EDA AS PATTERN DISCOVERY
Dimensionality Reduction - Linear Methods
Dimensionality Reduction - Nonlinear Methods
Data Tours
Finding Clusters
Model-Based Clustering
Smoothing Scatterplots
GRAPHICAL METHODS FOR EDA
Visualizing Clusters
Distribution Shapes
Multivariate Visualization
APPENDIX A: PROXIMITY MEASURES
APPENDIX B: SOFTWARE RESOURCES FOR EDA
APPENDIX C: DESCRIPTION OF DATA SETS
APPENDIX D: INTRODUCTION TO MATLAB
APPENDIX E: MATLAB FUNCTIONS
Introduction to Exploratory Data Analysis
EDA AS PATTERN DISCOVERY
Dimensionality Reduction - Linear Methods
Dimensionality Reduction - Nonlinear Methods
Data Tours
Finding Clusters
Model-Based Clustering
Smoothing Scatterplots
GRAPHICAL METHODS FOR EDA
Visualizing Clusters
Distribution Shapes
Multivariate Visualization
APPENDIX A: PROXIMITY MEASURES
APPENDIX B: SOFTWARE RESOURCES FOR EDA
APPENDIX C: DESCRIPTION OF DATA SETS
APPENDIX D: INTRODUCTION TO MATLAB
APPENDIX E: MATLAB FUNCTIONS