#### 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

## Introduction to k-modes Clustering

All the types of clustering that we have studied so far are based on a distance metric. But what if we get a dataset in which it's not possible to measure the distance between variables in a traditional sense, as in the case of categorical variables? In such cases, we use k-modes clustering.

k-modes clustering is an extension of k-means clustering, dealing with modes instead of means. One of the major applications of k-modes clustering is analyzing categorical data such as survey results.

### Steps for k-Modes Clustering

In statistics, mode is defined as the most frequently occurring value. So, for k-modes clustering, we're going to calculate the mode of categorical values to choose centers. So, the steps to perform k-modes clustering are as follows:

1. Choose any k number of random points as cluster centers.

2. Find the Hamming distance (discussed in Chapter 1, Introduction to Clustering Methods) of each point from the center.

3. Assign each point to a cluster whose center...