Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Table of Contents (21 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Visualizing a dissimilarity matrix


A dissimilarity matrix can be used as a measurement for the quality of a cluster. To visualize the matrix, we can use a heat map on a distance matrix. Within the plot, entries with low dissimilarity (or high similarity) are plotted darker, which is helpful to identify hidden structures in the data. In this recipe, we will discuss some techniques that are useful for visualizing a dissimilarity matrix.

Getting ready

In order to visualize the dissimilarity matrix, you need to have the previous recipe completed by generating the customer dataset. In addition to this, a k-means object needs to be generated and stored in the km variable.

How to do it...

Perform the following steps to visualize the dissimilarity matrix:

  1. First, install and load the seriation package:
        > install.packages("seriation")
        > library(seriation)
  1. You can then use dissplot to visualize the dissimilarity matrix in a heat map:
        > dissplot(dist(customer), labels=km$cluster...