Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Applied Unsupervised Learning with R
Applied Unsupervised Learning with R

Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Arrow left icon
Profile Icon Alok Malik Profile Icon Bradford Tuckfield
Arrow right icon
₱1431.99 ₱1591.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (10 Ratings)
eBook Mar 2019 320 pages 1st Edition
eBook
₱1431.99 ₱1591.99
Paperback
₱1989.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Alok Malik Profile Icon Bradford Tuckfield
Arrow right icon
₱1431.99 ₱1591.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (10 Ratings)
eBook Mar 2019 320 pages 1st Edition
eBook
₱1431.99 ₱1591.99
Paperback
₱1989.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
₱1431.99 ₱1591.99
Paperback
₱1989.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Table of content icon View table of contents Preview book icon Preview Book

Applied Unsupervised Learning with R

Chapter 2. Advanced Clustering Methods

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Perform k-modes clustering

  • Implement DBSCAN clustering

  • Perform hierarchical clustering and record clusters in a dendrogram

  • Perform divisive and agglomerative clustering

Note

In this chapter, we will have a look at some advanced clustering methods and how to record clusters in a dendrogram.

Introduction


So far, we've learned about some of the most basic algorithms of unsupervised learning: k-means clustering and k-medoids clustering. These are not only important for practical use, but are also important for understanding clustering itself.

In this chapter, we're going to study some other advanced clustering algorithms. We aren't calling them advanced because they are difficult to understand, but because, before using them, a data scientist should have insights into why he or she is using these algorithms instead of the general clustering algorithms we studied in the last chapter. k-means is a general-purpose clustering algorithm that is sufficient for most cases, but in some special cases, depending on the type of data, advanced clustering algorithms can produce better results.

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...

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...

Summary


Congratulations on completing the second chapter on clustering techniques! With this, we've covered all the major clustering techniques, including k-modes, DBSCAN, and both types of hierarchical clustering, and we've also looked at what connects them. We can apply these techniques to any type of dataset we may encounter. These new methods, at times, also produced better results on the same dataset that we used in the first chapter. In the next chapter, we're going to study probability distributions and their uses in exploratory data analysis.

Left arrow icon Right arrow icon

Key benefits

  • Build state-of-the-art algorithms that can solve your business' problems
  • Learn how to find hidden patterns in your data
  • Revise key concepts with hands-on exercises using real-world datasets

Description

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.

Who is this book for?

Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

What you will learn

  • Implement clustering methods such as k-means, agglomerative, and divisive
  • Write code in R to analyze market segmentation and consumer behavior
  • Estimate distribution and probabilities of different outcomes
  • Implement dimension reduction using principal component analysis
  • Apply anomaly detection methods to identify fraud
  • Design algorithms with R and learn how to edit or improve code

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 27, 2019
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781789951462
Category :
Languages :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Product Details

Publication date : Mar 27, 2019
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781789951462
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just ₱260 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just ₱260 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 6,225.97
Hands-On Time Series Analysis with R
₱1989.99
R Machine Learning Projects
₱2245.99
Applied Unsupervised Learning with R
₱1989.99
Total 6,225.97 Stars icon

Table of Contents

6 Chapters
Introduction to Clustering Methods Chevron down icon Chevron up icon
Advanced Clustering Methods Chevron down icon Chevron up icon
Probability Distributions Chevron down icon Chevron up icon
Dimension Reduction Chevron down icon Chevron up icon
Data Comparison Methods Chevron down icon Chevron up icon
Anomaly Detection Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(10 Ratings)
5 star 80%
4 star 20%
3 star 0%
2 star 0%
1 star 0%
Filter icon Filter
Top Reviews

Filter reviews by




Xin Chen May 15, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I would adopt this as a textbook if I teach a class on unsupervised learning.
Amazon Verified review Amazon
Catherine Apr 28, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is very helpful in understanding the important unsupervised learning methods, such as different clustering methods, PCA. It provides great examples to use R to solve problems. Excellent for beginner to follow and to be able to use R to do the analysis. The examples are all business questions, making it easier to relate to my business questions. In addition to that, this book explains the theory behind the algorithms in languages that are easy to understand. Overall, it is an amazing book for me to study and apply unsupervised learning algorithms!
Amazon Verified review Amazon
Don Mar 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book covers a broad range of topics, including the most common unsupervised learning ideas like clustering, as well as other ideas that are more rarely discussed. The code is useful and easy to understand. I particularly liked the introduction of silhouette scores and other methods for determining how many clusters to use in k-means clustering.
Amazon Verified review Amazon
Tanguy DESCHUYTENEER Feb 04, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Lotlamoreng Mosiane Dec 05, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Udemy Verified review Udemy
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

How do I buy and download an eBook? Chevron down icon Chevron up icon

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

  • You may make copies of your eBook for your own use onto any machine
  • You may not pass copies of the eBook on to anyone else
How can I make a purchase on your website? Chevron down icon Chevron up icon

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

  1. Register on our website using your email address and the password.
  2. Search for the title by name or ISBN using the search option.
  3. Select the title you want to purchase.
  4. Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title. 
  5. Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)
Where can I access support around an eBook? Chevron down icon Chevron up icon
  • If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
  • To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.