Exploratory Data Analysis with MATLAB - Angel R Martinez

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Auteur(s) : Angel R Martinez - Jeffrey Solka - Wendy L Martinez

Editeur : Crc Press

Langue : Anglais

Parution : 01/07/2017

Format : Moyen, de 350g à 1kg

Nombre de pages : 590

Expédition : 998

Dimensions : 23.6 x 15.7 x 4.1

ISBN : 9781498776066

Résumé :

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.

Biographie:

Wendy L. Martinez is a mathematical statistician with the U.S. Bureau of Labor Statistics. She is a fellow of the American Statistical Association, a co-author of several popular Chapman & Hall/CRC books, and a MATLAB(R) user for more than 20 years. Her research interests include text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition. She earned an M.S. in aerospace engineering from George Washington University and a Ph.D. in computational sciences and informatics from George Mason University.

Angel R. Martinez is fully retired after a long career with the U.S. federal government and as an adjunct professor at Strayer University, where he taught undergraduate and graduate courses in statistics and mathematics. Before retiring from government service, he worked for the U.S. Navy as an operations research analyst and a computer scientist. He earned an M.S. in systems engineering from the Virginia Polytechnic Institute and State University and a Ph.D. in computational sciences and informatics from George Mason University.

Since 1984, Jeffrey L. Solka has been working in statistical pattern recognition for the Department of the Navy. He has published over 120 journal, conference, and technical papers; has won numerous awards; and holds 4 patents. He earned an M.S. in mathematics from James Madison University, an M.S. in physics from Virginia Polytechnic Institute and State University, and a Ph.D. in computational sciences and informatics from George Mason University.

Sommaire:

Introduction to Exploratory Data Analysis. Dimensionality Reduction - Linear Methods. Dimensionality Reduction - Nonlinear Methods. Data Tours. Finding Clusters. Model-Based Clustering. Smoothing Scatterplots. Visualizing Clusters. Distribution Shapes. Multivariate Visualization. Appendices.