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)

Introduction


Data analysis often begins with an implicit assumption that all observations are valid, accurate, and trustworthy. But this is not always a reasonable assumption. Consider the case of credit card companies, who collect data consisting of records of charges to an individual's credit card. If they assumed that all charges were valid, they would open the door to thieves and fraudsters to take advantage of them. Instead, they examine their transaction datasets and look for anomalies – transactions that deviate from the general observed pattern. Since fraudulent transactions are not labeled, they have to use unsupervised learning to find these anomalies and prevent criminal activity.

There are many other situations in which anomaly detection is useful. For example, manufacturers may use anomaly detection methods to find defects in their products. Medical researchers may look for anomalies in otherwise regular heartbeat patterns to diagnose illnesses. IT security professionals try...