Book Image

Applied Unsupervised Learning with R

By : Alok Malik, Bradford Tuckfield
Book Image

Applied Unsupervised Learning with R

By: Alok Malik, Bradford Tuckfield

Overview of this book

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.
Table of Contents (9 chapters)

Latent Variable Models – Factor Analysis


This section will cover latent variable models. Latent variable models attempt to express data in terms of a small number of variables that are hidden or latent. By finding the latent variables that correspond to a dataset, we can better understand the data and potentially even understand where it came from or how it was generated.

Consider students receiving grades in a wide variety of classes, from math to music to foreign languages to chemistry. Psychologists or educators may be interested in using this data to better understand human intelligence. There are several different theories of intelligence that researchers might want to test in the data, for example:

  • Theory 1: There are two different types of intelligence, and people who possess one type will excel in one set of classes, while people who possess the other type will excel in other classes.

  • Theory 2: There is only one type of intelligence, and people who possess it will excel at all types...