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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

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Profile Icon Alok Malik Profile Icon Bradford Tuckfield
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€28.99
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Paperback Mar 2019 320 pages 1st Edition
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Arrow left icon
Profile Icon Alok Malik Profile Icon Bradford Tuckfield
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€28.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (10 Ratings)
Paperback Mar 2019 320 pages 1st Edition
eBook
€15.99 €22.99
Paperback
€28.99
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Free Trial
Renews at €18.99p/m
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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.

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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
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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

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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.
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