#### 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.
Applied Unsupervised Learning with R
Preface
Free Chapter
Introduction to Clustering Methods
Probability Distributions
Dimension Reduction
Data Comparison Methods
Anomaly Detection

## Comparison of Signatures

Next, we can compare these two signatures, to see whether they have mapped our different images to different signature values.

You can compare the signatures with one simple line of R code as follows:

`comparison<-mean(abs(borges_signature-building_signature))`

This comparison takes the absolute value of the difference between each element of the two signatures, and then calculates the mean of those values. If two signatures are identical, then this difference will be 0. The larger the value of comparison, the more different the two images are.

In this case, the value of comparison is 0.644, indicating that on average, the corresponding signature entries are about 0.644 apart. This difference is substantial for a dataset where the values only range between 1 and -1. So we see that our method for creating signatures has created very different signatures for very different images, as we would expect.

Now, we can calculate a signature for an image that is very similar...