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.

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Table of Contents (9 chapters)
Applied Unsupervised Learning with R
Applied Unsupervised Learning with R
Preface
Preface
Free Chapter
1
Introduction to Clustering Methods
Introduction to Clustering Methods
Introduction
Introduction to Clustering
Introduction to the Iris Dataset
Introduction to k-means Clustering
Introduction to k-means Clustering with Built-In Functions
Introduction to Market Segmentation
Introduction to k-medoids Clustering
Summary
2
Advanced Clustering Methods
Advanced Clustering Methods
Introduction
Introduction to k-modes Clustering
Introduction to Density-Based Clustering (DBSCAN)
Summary
3
Probability Distributions
Probability Distributions
Introduction
Basic Terminology of Probability Distributions
Introduction to Kernel Density Estimation
Introduction to the Kolmogorov-Smirnov Test
Summary
4
Dimension Reduction
Dimension Reduction
Introduction
Market Basket Analysis
Summary
5
Data Comparison Methods
Data Comparison Methods
Introduction
Analytic Signatures
Comparison of Signatures
Latent Variable Models – Factor Analysis
Summary
6
Anomaly Detection
Anomaly Detection
Introduction
Univariate Outlier Detection
Kernel Density
Summary
Appendix
Appendix
Chapter 1: Introduction to Clustering Methods
Chapter 2: Advanced Clustering Methods
Chapter 3: Probability Distributions
Chapter 4: Dimension Reduction
Chapter 5: Data Comparison Methods
Chapter 6: Anomaly Detection

Introduction

The 21st century is the digital century, where every person on every rung of the economic ladder is using digital devices and producing data in digital format at an unprecedented rate. 90% of data generated in the last 10 years was generated in the last 2 years. This is an exponential rate of growth, where the amount of data is increasing by 10 times every 2 years. This trend is expected to continue for the foreseeable future:

Figure 1.1: The increase in digital data year on year

But this data is not just stored in hard drives; it's being used to make lives better. For example, Google uses the data it has to serve you better results, and Netflix uses the data it has to serve you better movie recommendations. In fact, their decision to make their hit show House of Cards was based on analytics. IBM is using the medical data it has to create an artificially intelligent doctor and to detect cancerous tumors from x-ray images.

To process this amount of data with computers and come up with relevant results, a particular class of algorithms is used. These algorithms are collectively known as machine learning algorithms. Machine learning is divided into two parts, depending on the type of data that is being used: one is called supervised learning and the other is called unsupervised learning.

Supervised learning is done when we get labeled data. For example, say we get 1,000 images of x-rays from a hospital that are labeled as normal or fractured. We can use this data to train a machine learning model to predict whether an x-ray image shows a fractured bone or not.

Unsupervised learning is when we just have raw data and are expected to come up with insights without any labels. We have the ability to understand the data and recognize patterns in it without explicitly being told what patterns to identify. By the end of this book, you're going to be aware of all of the major types of unsupervised learning algorithms. In this book, we're going to be using the R programming language for demonstration, but the algorithms are the same for all languages.

In this chapter, we're going to study the most basic type of unsupervised learning, clustering. At first, we're going to study what clustering is, its types, and how to create clusters with any type of dataset. Then we're going to study how each type of clustering works, looking at their advantages and disadvantages. At the end, we're going to learn when to use which type of clustering.

Previous Section
End of Section 2
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