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 (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 is the first book to put a computational emphasis on the methods used to visualize and summarize data before making model assumptions to generate hypotheses. The authors use MATLAB code and algorithmic descriptions to provide the user with state-of-the-art techniques for finding patterns and structure in data. They also focus on the computational aspects of these methodologies as opposed to theoretical. Many annotated references to papers and books help to provide the theoretical aspects of the topic. The approach taken by the authors helps to make exploratory data analysis accessible to a wide range of users "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."--BOOK JACKET This book is divided into two main sections: pattern discovery and graphical EDA.
