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 to Density-Based Clustering (DBSCAN)

Density-based clustering or DBSCAN is one of the most intuitive forms of clustering. It is very easy to find naturally occurring clusters and outliers in data with this type of clustering. Also, it doesn't require you to define a number of clusters. For example, consider the following figure:

Figure 2.2: A sample scatter plot

There are four natural clusters in this dataset and a few outliers. So, DBSCAN will segregate the clusters and outliers, as depicted in the following figure, without you having to tell it how many clusters to identify in the dataset:

Figure 2.3: Clusters and outliers classified by DBSCAN

So, DBSCAN can find regions of high density separated by regions of low density in a scatter plot.

Steps for DBSCAN

As mentioned before, DBSCAN doesn't require you to choose a number of clusters, but you have to choose the other two parameters to perform DBSCAN. The first parameter is commonly denoted by ε (epsilon), which denotes the maximum...