This section briefly introduces the author, the coverage of this book, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions.
This book begins with the most important and commonly used method for unsupervised learning – clustering – and explains the three main clustering algorithms: k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models.
By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.
Alok Malik is a data scientist based in India. He has previously worked on creating and deploying unsupervised learning solutions in fields such as finance, cryptocurrency trading, logistics, and natural language processing. He has a bachelor's degree in technology from the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, where he studied electronics and communication engineering.
Bradford Tuckfield has designed and implemented data science solutions for firms in a variety of industries. He studied math for his bachelor's degree and economics for his Ph.D. He has written for scholarly journals and the popular press, on topics including linear algebra, psychology, and public policy.
Design clever algorithms that discover hidden patterns and business-relevant insights from unstructured, unlabeled data.
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all the features of R that enable you to understand your data better and get answers to all your business questions.
Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.
Applied Unsupervised Learning with R takes a hands-on approach to using R to reveal the hidden patterns in your unstructured data. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
For the optimal student experience, we recommend the following hardware configuration:
We also recommend that you have the following software installed in advance:
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We import a factoextra library for visualization of the clusters we just created."
A block of code is set as follows:
plot(iris_data$Sepal.Length,iris_data$Sepal.Width,col=iris_data$t_color)
points(k1[1],k1[2],pch=4)
points(k2[1],k2[2],pch=5)
points(k3[1],k3[2],pch=6)
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "There are many different types of algorithms for performing k-medoids clustering. The simplest and most efficient of them is Partitioning Around Medoids, or PAM for short."
Each great journey begins with a humble step. Our upcoming adventure in the land of data wrangling is no exception. Before we can do awesome things with data, we need to be prepared with the most productive environment. In this small note, we shall see how to do that.
To install R on Windows, follow these steps:
To install R on macOS X, perform the following:
sudo apt update
sudo apt install r-base
In this case, we wrote apt as the package manager, but if your version of Linux uses yum or some other package manager, you should replace every apt in these two lines with yum or the name of your package manager.