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>

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Unsupervised Learning with R
Erik Rodríguez Pacheco
Dec, 2015 | 192 pages
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Table of Contents (15 chapters)
Unsupervised Learning with R
Unsupervised Learning with R
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Welcome to the Age of Information Technology
Welcome to the Age of Information Technology
The information age
Information theory
Data mining methodology and software tools
Benefits of using R
Summary
2
Working with Data – Exploratory Data Analysis
Working with Data – Exploratory Data Analysis
Exploratory data analysis
Loading a dataset
Basic exploration of the dataset
Exploring data by basic visualization
Exploring relations in data
Exploration by end-user interfaces
Summary
3
Identifying and Understanding Groups – Clustering Algorithms
Identifying and Understanding Groups – Clustering Algorithms
Transforming data
Fundamentals of clustering techniques
Clustering by end-user interfaces
Summary
4
Association Rules
Association Rules
Fundamentals of association rules
Exploring the association rules model
Plotting alternatives for association rules
Association rules by end-user tool
Summary
5
Dimensionality Reduction
Dimensionality Reduction
The curse of dimensionality
Feature extraction
Summary
6
Feature Selection Methods
Feature Selection Methods
Feature selection techniques
Summary
References
References
Chapter 1, Welcome to the Age of Information Technology
Chapter 2, Working with Data – Exploratory Data Analysis
Chapter 3, Identifying and Understanding Groups – Clustering Algorithms
Chapter 4, Association Rules
Chapter 5, Dimensionality Reduction
Chapter 6, Feature Selection Methods
Index
Index

About the Reviewer

Nicholas A. Yager is a biostatistician and software developer researching statistical genomics, image analysis, and infectious disease epidemiology. With an education in biochemistry and biostatistics, his experience analyzing cutting-edge genomics data and simulating complex biological systems has given him an in-depth understanding of scientific computing and data analysis. Currently, Nicholas works for a personalized medicine company, designing medical informatics systems for next-generation personalized cancer tests. Apart from this book, Nicholas has reviewed Mastering Rstudio: Develop, Communicate, and Collaborate with R, Julian Hillebrand, Maximilian H. Nierhoff, Packt Publishing.

I would like to thank my friends, Lauren and Matt, and my mentor, Dr. Gregg Hartvigsen, for their help with this book.

Nicolas Turenne is a PhD in computer science and a research fellow at the French National Institute for Agricultural Research (INRA). He is also in the Interdisciplinary Laboratory Sciences Innovations Societies (LISIS), UMR 1326 at Paris-Est University.

He is an expert in data mining and knowledge discovery from text databases using stochastic and relational models; applications of which are life sciences, security, and social media analysis.

He has written books such as Knowledge Needs and Information Extraction: Towards an Artificial Consciousness in March 2013 by Wiley-ISTE and Analyse de données textuelles sous R, which will be published in January 2016 by ISTE.

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