Book Image

Unsupervised Learning with R

By : Erik Rodríguez Pacheco
Book Image

Unsupervised Learning with R

By: Erik Rodríguez Pacheco

Overview of this book

<p>The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning.</p> <p>If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console.</p> <p>Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques.</p> <p>By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects.</p>
Table of Contents (15 chapters)
Unsupervised Learning with R
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

Acknowledgments

The author of this book is not the creator of any of the packages, functions, or programs used in any of the examples, he is only a facilitator.

For that reason, I would like to sincerely thank the developers of R and R packages, who have contributed so generously to the growing of the R open source community. In this book, we used many packages. Sometimes, the definitions of these packages, in order to be respectful to the authors, are written literally. The Appendix at the end of the book contains all sources as special thanks to the authors.

I would like to thank my data mining professor PhD Oldemar Rodriguez Rojas, who inspired me and taught me so much.

I would also like to thank my publisher, Packt Publishing, for giving me the opportunity to work on this book. I would like to thank all the technical reviewers and content development editors at Packt Publishing for their informative comments and suggestions.

I would like to thank Felix Alpizar Lobo and Irene Gallegos Gurdian from Banco Improsa for all their support and mentoring.

Finally, I would like to thank my amazing wife, Silvia, without her encouragement, support, and patience, this book would not have been possible.