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 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...

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