Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Chapter 8. Cluster Analysis

"Quickly bring me a beaker of wine, so that I may wet my mind and say something clever."                                                                                 - Aristophanes, Athenian Playwright

In the earlier chapters, we focused on trying to learn the best algorithm in order to solve an outcome or response, for example, a breast cancer diagnosis or level of Prostate Specific Antigen. In all these cases, we had y, and that y is a function of x, or y = f(x). In our data, we had the actual y values and we could train the x accordingly. This is referred to as supervised learning. However, there are many situations where we try to learn something from our data and either we do not have the y or we actually choose to ignore it. If so, we enter the world of unsupervised learning. In this world, we build and select our algorithm based on how well it addresses our business needs versus how accurate it is.

Why would we try and learn without supervision? First...