Introduction
Unsupervised learning is concerned with analyzing the structure of data to draw useful conclusions. In this chapter, we will examine methods that enable us to use the structure of data to compare datasets. The major methods we will look at are hash functions, analytic signatures, and latent variable models.
Hash Functions
Imagine that you want to send an R script to your friend. However, you and your friend have been having technical problems with your files – maybe your computers have been infected by malware, or maybe a hacker is tampering with your files. So, you need a way to ensure that your script is sent intact to your friend, without being corrupted or changed. One way to check that files are intact is to use hash functions.
A hash function can create something like a fingerprint for data. What we mean by a fingerprint is something that is small and easy to check that enables us to verify whether the data has the identity we think it should have. So, after you create the...
